From the start of a synthetic chemist’s training,experimentsare conducted based on recipes from textbooks and manuscripts thatachieve clean reaction outcomes, allowing the scientist to developpractical skills and some chemical intuition. This procedure is oftenkept long into a researcher’s career, as new recipes are developedbased on similar reaction protocols, and intuition-guided deviationsare conducted through learning from failed experiments. However, whenattempting to understand chemical systems of interest, it has beenshown that model-based, algorithm-based, and miniaturized high-throughputtechniques outperform human chemical intuition and achieve reactionoptimization in a much more time- and material-efficient manner; thisis covered in detail in this paper. As many synthetic chemists arenot exposed to these techniques in undergraduate teaching, this leadsto a disproportionate number of scientists that wish to optimize theirreactions but are unable to use these methodologies or are simplyunaware of their existence. This review highlights the basics, andthe cutting-edge, of modern chemical reaction optimization as wellas its relation to process scale-up and can thereby serve as a referencefor inspired scientists for each of these techniques, detailing severalof their respective applications. This isparticularly true in academic research, where intuition-based optimization is commonplace despite the increasing evidence showing that reactionmodeling and algorithmic optimizations are more efficient in relationto both time and material and therefore cost.16 For these reasons, the use ofthese methodologies is much more widespread in industrial researchand development, particularly in process laboratories compared totheir discovery laboratory counterparts,18 as manufacturing conditions often result from vigorous optimizationprotocols. Consequently, there is often a large disparity in the familiarityof optimization techniques between industrial and academic researchers,particularly because industrial scientists also often have internalmultidisciplinary teams of statisticians and process chemists to collaboratewith. However, the techniques covered are not inaccessible for chemists,and increasing the exposure of these methodologies will make themmore widespread across both academic research and teaching, therebyenriching the skillset of the entire chemical community. Althoughthe primary aims of many scientists (particularly synthetic chemists)may not be to achieve truly optimal processes, familiarity with theconcepts discussed herein will help researchers meet the needs ofthe modern and evolving laboratory.Chemical reaction optimizationis a term that has a variety ofmeanings depending on the chemist defining it, with a large correspondingvariance in expectations of optimization capability and proficiency.As reaction optimization is largely unexplored during undergraduatechemistry teaching,E-factor, enantiomeric excess,etc., and these concepts will be explored further. Typical reactionvariables that are optimized are also often described as either continuous or categorical. Furtherin-depth reading will also be provided at each stage for interestedscientists seeking a deeper understanding of the workings of eachmethodology. We also discuss how to explore the generated reactionknowledge within the subsequent process scale-up efforts. By providingtools for considerations of scale-up challenges and complexity inthe early stages of process optimization, we hope to help chemiststo guide their optimization efforts toward scalable processes andthus facilitate the translation of critical laboratory discoveriesinto commercially available products.This review aims to criticallyanalyze and compare major chemicalreaction optimization techniques, thereby helping to deliver an accessibleaccount of optimization strategies (with references to their applications)for the general chemical scientist. As many of these methodologiesborrow concepts from related fields, such as statistics, computerscience, process chemistry, and engineering, this review will helpto diversify the chemist’s toolkit and serve as a comprehensiblereference for optimization campaigns. Although reaction optimizationis often related to reaction yields, it may also be performed withrespect to purity, 219 This is primarily performed in academia and in the presence of astructured, yet simple to follow, procedure, making this techniqueseem both effective and accessible. The OFAT methodology itself requiressome scientific intuition, where experiments are iteratively performedby fixing all process factors except for one. After the best valuefor the one factor has been identified, that value is fixed whileanother set of experiments are executed to optimize another factoruntil each factor is optimized and the scientist believes that theyhave arrived at the optimum reaction conditions.20 These factors can be any number of experimental conditions which, whencombined, constitute a multidimensional space with many possible combinationsof factors to make up one experiment. This is termed the parameterspace and is constrained by the upper and lower limits of each factor.“Intuition-basedoptimization” largely relates tooptimization using the trends and anecdotal observations from experiencedchemists to improve reaction metrics. Alongside optimization via chemicalintuition, one factor at a time (OFAT) approaches often substituteas a method for chemical process optimization and understanding.21 Interactionsbetween the experimental factors are ignored, as this linear experimentalprocedure is applied to chemical reaction outputs that give exclusivelynonlinear responses.22 This nonlinearitycan be explained by statistical or physical modeling but is not exploredusing OFAT, which therefore often incorrectly identifies true optimalreaction conditions.24 An exemplar schematic of an OFATcampaign is shown in The OFAT approach isoften inaccurate and inefficient as an optimizationtechnique, and the method frequently misinterprets the chemical processas there are no considerations for any synergistic effects betweenthe factors considered.25 shows the use of OFAT optimization to achieve fair yieldsin the synthesis of bioactive propargylamine scaffolds. The modelreaction was optimized, shown in Generally, scientists perform OFAT campaigns asthe method canbe performed without mathematical modeling, which is a critical advantagewhen performing lab-based experiments and is one major reason it isprevalent. A recent example by Abtahi and Tavakol28 In our own laboratories, when undergraduate chemistsare given the task of optimizing a reaction, students will often employOFAT techniques as they are unaware of other means of optimization.29 This is not the fault of the student, nor isit the fault of the academics optimizing their reactions in this way,as this does perform as a rudimentary technique to achieve improvedreaction yields. However, as research laboratories are beginning todiversify their equipment by incorporating advanced technologies suchas automated retrosynthesis software and experimentation,31 it is also important for chemists to evolve at the same pace bydiversifying their own skillsets to fully harness the capabilitiesof the evolving laboratory. Synthetic chemists, in recent years, havebegun to embrace facets of process chemistry, chemical engineering,analytical chemistry, and computer science, to name a few.18 Concurrently, it is important to facilitate better understandingand adoption of reaction optimization methods as OFAT optimizationsare superseded by more robust and more efficient techniques.34 Chemical reaction optimization by OFAT has therefore been includedin this paper for comparative purposes.There are several examples from the literatureof this technique’sapplication to various chemistries, in many cases with different levelsof applied chemical intuition but still following the structure ofthe OFAT methodology.323 There are three main objectives for DoE: screening, optimization,and robustness.35 Screening involves theidentification of factors that have a significant effect on reactionoutput, as well as their respective upper and lower bounds. Optimizationfocuses on the determination of the optimum factor levels, such asthe optimum temperature and reagent equivalents, to achieve the bestreaction output possible. Finally, robustness testing involves theidentification of the sensitivity of this response to small changesin the experimental factors; this is important on a process scaleto understand how possible deficiencies in reactors may lead to suboptimaloutputs.One robustand widely used optimization technique, particularlyin the pharmaceutical and fine chemical industries, is design of experiments(DoE). DoE is a class of statistical methods that aim to build a modelthat can mathematically describe the output of a chemical reaction based on the experimental inputsfor that reaction (factors such as temperature or reaction time).There are many reports of widespread DoE usage for reaction optimization,but it is also often used in the literature as a comparison with OFAToptimizations to highlight its efficiency and thereby refute OFAT.36 The format of this experimentaldata is important and often difficult to replicate/analyze using humanintuition, which is why DoE software is often implemented, such asMODDE,37 JMP,38 Design-Expert,39 or toolbox applicationsin languages such as R, MATLAB, and Python. After data collectionand the fitting of the statistical model, optimized process parameterscan then be identified, and response surfaces are often plotted tohelp visualize the effects of experimental factors on the chemicaloutput.The practical manner of running DoE campaigns focuseson performingpredefined experiments from a structured experimental design. Thesedesigns are templates from which to execute experiments, based onthe factors and bounds of interest, that explore the parameter spaceefficiently and provide data in a structured format to build robuststatistical models.NAr reaction of 2,4-difluoronitrobenzenewith pyrrolidine, as shown in 1 This reaction has multipleproducts, but the study aimed to produce the ortho-substituted product, 7, in the highest yield by using a face-centered central composite(CCF) design; more details on specific DoE designs are outlined in 7. After statistical analysis, a response surface was plotted forthe chemical process to visualize the effect of each factor on theyield of our ortho-substituted product, as shown in 7 could be obtained by using higher temperature,longer residence times, and higher pyrrolidine equivalents, leadingto a yield of 93%. However, these reaction conditions also producedthe highest yield of the impurity, the disubstituted product, 9. A process chemist could then use this information to decidefor a particular reactor system if product throughput is more importantthan other downstream processes, such as product purity and separation.One optimization campaign performed using DoE in ourlaboratoryis the multistep S40 on the synthesisof vanillin, iso-vanillin, and heliotropin. SeveralDoE studies were employed to identify optimum process factors foreach synthetic step, one of which was the initial addition of glyoxylicacid, 11, to catechol, 10, to form the desired3,4-dihydroxymandelic acid intermediate, 12, as shownin Another campaign highlighting the effectivenessof DoE was reportedby Minisci and co-workers4 + 2 “center” experiments).However, it was found that an excess of sodium hydroxide results inmuch greater rates of impurity formation, hence another study wasperformed under fixed, less basic conditions. The resulting threefactors were therefore inputted into the second full factorial designof nine experiments (23 + 1), where the responses of recoveredcatechol, selectivity of desired product, and yield of desired productwere measured. A statistical model was constructed for each response,and the response surface for the selectivity of the desired intermediate, 12, is plotted in The first DoE studyexplored the following factors: amount of glyoxylicacid, amount of aluminum oxide, reaction temperature, and amount ofsodium hydroxide, while fixing the amount of catechol, volume of water,and reaction time at convenient levels. This design resulted in 18experiments , which may have classicallyhindered the uptake of the technique, particularly for smaller researchorganizations. However, software options have undoubtedly helped tofacilitate the employment of DoE overall due to the high expertisebarrier for typical bench scientists to use the statistical methodsunaided. Another major disadvantage is the difficulty in exploringcategorical variables in DoE studies, as these experimental designsare only suitable for continuous variables. One way to incorporatecategorical variables, such as solvent or catalyst, would be to describethem with suitable continuous descriptors that can then be translatedto real-world categorical choices; see more information on molecularparametrization in There are, however, some practical disadvantagesto using DoE foroptimization that may lead to the necessity of employing other techniques.Although some research groups utilize coding  and statistical expertise to perform DoEcampaigns,44 For example, if the reaction time is explored as a factor as partof a DoE study between the bounds of 5–30 min, extrapolatingthe model to predict responses after 60 min would likely result ininaccuracies, and further study must be conducted to predict theseoutputs. Furthermore, the number of experiments that are requiredto be performed in parallel for some DoE studies may be large, dependingon the amount of reaction material or time required to conduct theseexperiments, this may be prohibitive in some circumstances.45 However, the rise of highly automated experimentalplatforms with online analytics provides a powerful option for chemicalprocess development in many cases by miniaturizing and automatingexperimentation using DoE.As DoE builds a statistical model, it has only anempirical meaningrather than physical, therefore, there is no ingrained physicochemicalinformation about the optimized process within the model; this meansthat model responses are only considered to be accurate within theexplored bounds of the experimental factors.57 and purity62 of particular products but is also used for drug formulations68 and delivery,71 analytical method development,75 and more.79 This is because there are numerousand undisputed benefits to the use of DoE for experimental parameterscreening and optimization, especially when compared with traditionalhuman intuition-guided experimentation. With the rise of user-friendlysoftware packages and the increased awareness of the chemical community,there has been a large uptake of this statistical method in recentyears, although the technique itself has been around since the mid-20thcentury.34 As the advantages of DoE areharnessed, and more industrial job roles will require familiaritywith the technique,80 an organic evolution of academicdepartments will also occur where there is more teaching of the topicand utilization of the methods for optimization practices. It is thereforea possibility in the future that DoE optimization becomes routine,regardless of the research setting, where the modernized laboratorycontains enabling equipment for these studies, with chemists possessinghighly diversified skillsets.DoE has been used extensively inthe optimization of chemical processes,particularly in pharmaceutical and fine chemical settings. DoE isoften used for studies relating to enhancing the yield3.1x1 is variable 1 , x2 is variable 2 , y is the experimental response (such as reaction yield)and bn is the coefficientterm for the variable(s) of interest (determined by regression). Therefore,by replacing x1 and x1 with actual values for the factors considered, it ispossible to predict the response for any experiment, including experimentswith reaction conditions that have not actually been conducted. Asany point in the parameter space can be predicted this way, the entirespace can also be represented graphically as a contour plot; thisallows the behavior of the factors to be more easily understood andinterpreted, as well as allowing predicted response maxima to be easilyidentified. The response is often also mathematically transformedto give better predictability between two bounds, e.g., log 10 transformedso that a yield prediction cannot exceed 100%.A mathematical,empirical model is featured at the center of each DoE study, builtfrom real-world chemical experiments that relate experimental factorsto chemical responses. The general format for the model involves fittingcoefficients for each experimental factor, as well as for each 2-factor-combinationpossible. Therefore, the model for a 2-factor experiment is shownin 82 These interactionterms within the model indicate how the experimental factors influencereaction output, when other factors are also changed alongside them.When considering the optimization of some generic reaction while exploringthe factors of reagent equivalents and temperature, it may be a significantfactor in the modeling of the data to include an interaction termbetween these factors. In real terms, this interaction could indicatethat a higher temperature has a positive influence on the reactionoutput only at higher reagent equivalents. Similarly, these factorsmay have an interaction term with themselves, as described by a squaredterm. For example, a squared temperature model term could indicatethat temperature has a larger effect on the response at elevated temperatures,meaning that temperature has a nonlinear effect on the reaction withinthe explored parameter space. These interaction considerations cantypically give a better description of the experimental data, as allsynergistic effects between the factors are incorporated into themodel. However, all experimental factor and interaction terms canbe added or removed from a DoE model depending on whether their contributionto the model is significant.As described in the empirical model,it is not only the experimental factors that have an effect on theresponse but also the interactions between these factors; this isan attribute of DoE that is difficult to reproduce through methodologiesthat utilize human chemical intuition.84 Depending on the practicality of running experiments, it may benecessary to balance model accuracy with experimental measurements,as some designs that feature several factors may require many moreexperiments at different levels for a marginal increase in predictivepower. For example, to estimate all terms for a model containing fiveexperimental factors, a three-level full factorial design would require246 experiments, while a face-centered central composite design wouldonly require 29 with a minimal reduction in predictive accuracy. Itis therefore important for the bench scientist to identify the optimalexperimental design for their purposes to avoid conducting unnecessaryexperimental observations, leading to additional time and materialcosts. For more detailed information on DoE designs, also refer toKumar and co-workers.35To accurately isolate and determineinteraction effects, specificDoE designs can be implemented. Within each design, the experimentalfactors are split into respective levels: these denote the degreeof the experimental factor and are conventionally labeled betweenthe minimum (−1) and maximum (+1). For example, a three-leveldesign that is exploring reaction temperature (10–50 °C)would use the levels −1, 0, and 1, which correspond to 10,30, and 50 °C, respectively. These levels are defined to ensurethat the entire parameter space can be explored regardless of thefactor range. Each experimental design may explore different levels,which also depend on the number of factors to be explored, in orderto identify specific interaction effects and remove confounding (uncertainty).3.234 Although there are several options for DoE designs, the statisticalknowledge necessary for chemists to select the correct experimentalprocedure and analyze the resulting data is low. Because of the relative easeof the technique, DoE can be easily taught to chemistry students andadopted for use in academic laboratories as intuition-based methodsare superseded.86 Furthermore, the ubiquity of DoE in process laboratories in industryhighlights that these statistical methodologies must be taught tochemists; this will help to develop the skillsets of the studentsand familiarize them with common optimization protocols that theyare likely to encounter in future. For further detailed reading onstatistical modeling within DoE, refer also to Telford87 and Severin and co-workers.88Chemical reaction optimizationusing design of experiments can be very powerful when attempting toidentify regions of optimal parameter space. The methodology has arelatively low expertise barrier-to-entry, given the advent of DoEsoftware, and provides bench scientists with tools to identify significantexperimental variables and model their data.490 rather than statisticalrelationships between experimental factors and outcomes. In contrastto DoE, undergraduate courses typically cover kinetic analysis indetail as part of their core physical chemistry modules, coveringtheories on collision, rate laws, and some basic physical-organicconcepts. However, the more practical uses for kinetic analysis  are seldom explored bymany chemists and are typically reserved for chemical and processengineers. This could be because of large expertise gaps experiencedby chemists, or simply because they are unaware of the benefits ofkinetic studies for their processes. Because of this distinct knowledgegap, practical kinetic analysis is discussed in this review and howit relates to reaction optimization. When these kinetic models areconstructed, they enable scientists to understand and simulate reactionsto determine optimal regions of parameter space in silico.The use of kineticmodeling, featuring a mechanistic model ratherthan a statistical one, is also common for reaction understandingand optimization, especially in process laboratories in industry andacademia. Kinetic models are constructed from a scientific understandingof the chemical process91 Written simply, this relates the order of a chemical species withinan elementary step to the number of molecules reacting within thatstep. For the reaction shown in x1–3 arechemical species and α/β/γ are their stoichiometriccoefficients), based on the law of mass action, the reaction ratecan be described by k, therefore determinesthe speed of the reaction as well as the reactant concentrations.Thephysical modeling of a reaction typically features the ratelaws of each individual chemical step and their corresponding rateconstants. The main assumption when using a physical model is thatthe reaction kinetics follow the law of mass action; this states thatthe rate of an elementary reaction is directly proportional to theproduct of the concentrations of the reactants, raised to the powerof their stoichiometric coefficients.97The law of mass action is applicablein almost all cases and istypically only inappropriate when concentrations of particular substratesare very low. More commonly encountered are very fast elementary reactionsthat occur in some processes, where it is more appropriate to describean entire reaction with an observed rate rather than the combinationof its individual elementary parts. This leads to circumstances wherechemical species that are reported to have a zero-order, second-orderor even noninteger-order dependence, as it is much more practicalto describe the physical model in this way.99 The determinationof reaction orders and rate laws within a model is used to optimizeoutputs but also to increase overall chemical understanding. As aresult of impurity formation upon scale-up, Ashworth and co-workersstudied the kinetics of the alkylation of the indolphenol, 16, with the chloropyrrolidine, 17, to form cediranib, 18, which is a pharmaceutical treatment for solid tumors  for an increase inoverall reaction yield.The physical models generated from kinetic analysis containingrainedchemical information that, unlike their empirical model counterparts,can be used to extrapolate reaction predictions outside of previouslyconducted experimental constraints.d tumors 4.100 The20, to 4-aminophenol, 21, using gold nanoparticles(AuNPs) by Chamberlain and co-workers  while a first-order reaction can be confirmed via a log-transformedplot of the data (b). For bimolecular reactions, if both reactantsare the same, then an overall second-order reaction can be confirmedsimply from the plotting of the reciprocal of the concentration data(c). However, when both reactants are not the same, more complex plottingcan confirm an overall second-order reaction (d). Additional modeling,such as the conventional modeling of enzymatic  reactions, known as Michaelis–Menten kinetics, canalso be applied; for more information refer to a recent review byJohnson.108When fittinga physical model of rate laws to a chemical process, the reactionorder and rate constants must be experimentally determined. For amechanistic model that incorporates reaction temperature, activationenergies must also be identified. Conventionally, mathematical transformationsto concentration–time data are applied to identify both thereaction order and rate constants in one plot; this can then be repeatedat multiple temperatures to obtain the activation energies for thereaction.109 confirmed second-order reaction kinetics ofa benzaldehyde oxidation, and work by Guégan and co-workers110 confirmed first-order reaction kinetics inan epoxybutane polymerization; both cases are recent examples of howthese conventional methodologies are still used. As can be seen from 111 or kinetic fitting software,such as Compunetics.112These techniques are still employed regularly todayin both teachingand research laboratories. For example, a study by Etua and co-workers4.2115 Although this is auseful strategy that is often employed by engineers,120 it is rarely conducted by chemists, as there is a high expertisebarrier to access this form of kinetic analysis, namely, coding andmathematical knowledge. Software solutions have been developed toaid in this kinetic model fitting  thatoften require minimal coding expertise, which have been adopted byprocess chemists but have still had relatively low uptake within thewider chemistry community. One example of fitting a kinetic modelusing rate constant solvers was shown in our lab for the determinationof the overall first-order reaction of alanine methyl-ester (Al-Me), 22, and 9-bromo-9-phenylfluorene (PfBr), 23,to form the protected amino acid (Pf-Al-Me), 24, as shownin 92 This approach computationally identified the“best-fit” parameters for the rate constants and activationenergies, with the fit to the experimental data shown in the combinedplot in In every chemicalcase, regardless of the complexity of the process, it is possibleto write coded solutions for kinetic analysis using rate constantsolvers .25, with the Hantzsch ester, 26, using the macrocycliccatalyst, 27, to yield the tetrahydroquinoline, 28, as shown in 131 This work highlighted, usingthe RPKA methodology, that the process was first-order with respectto both substrates and the catalyst, and that there was no observedcatalyst deactivation or product inhibition over time. Each pieceof information gained from this study can thereby help in furtherprocess development, both for reaction condition optimization andscale-up suitability.One recent example by Niemeyer and co-workers used RPKA intheregio- and stereoselective reduction of 2-phenylquinoline, 132 VTNA can obtainthe same chemical and physical model information as RPKA but doesnot require rate/time data; instead, VTNA can be used directly withconcentration/time data, which requires fewer experiments to obtainand less data manipulation.134 As VTNA requires onlysimple graphical transformations with easily obtained data, this methodologyhas also been reported in many process chemistry applications andchemical optimization studies.138Variable time normalization analysis (VTNA)is another techniquethat, alongside RPKA, falls into the dubbed category of “visualkinetic analysis”.N-benzylpicolinamide, 29, with 4-octyne, 30, to yield the dihydroisoquinoline product, 31, as shown in 139 This study showed that the reactionexhibited a first-order dependence on the cobalt concentration, azero-order dependence on the alkyne and a partial negative-order inthe benzylamide concentration. The authors suggested that this partialnegative-order finding was a result of off-cycle unproductive bindinginteractions with the catalyst, thereby decreasing the effective concentrationof catalyst available. Therefore, for further optimization work andscale-up, this quantification of reaction orders has been crucialin showing that there must be an optimal benzylamide concentrationrange whereby productivity is maximized.One recent example by Carretero andco-workers showed how VTNAwas utilized in the cobalt-catalyzed C–H functionalizationof 4.3142 As robust physical models are often useful for optimization andnecessary for scale-up, familiarity with these modeling techniquesis very important as our laboratories become more interdisciplinaryand connected.Kinetic modeling is a verypowerful tool for the optimization of chemical processes and representsa much more systematic approach than traditional OFAT optimization.The mechanistic models used in kinetic analysis also provide chemicalinsights and scientific understanding that DoE does not but may alsobe more difficult to interpret and conduct experimentally as time-seriesdata is paramount. Although software solutions have been created tolower the expertise barrier for kinetic analysis, the uptake of kineticmodeling techniques is still relatively low among chemistry researchersas the analysis is typically assigned to physical-organic or engineeringcolleagues, often unnecessarily as many reactions are simplistic andeasy to fit a physical model to. Visual kinetic analysis, althougha relatively new methodology, has helped to provide an accessibleframework for chemists to obtain semiquantitative model informationfrom their processes without the need for coding or software. Morecomplex techniques that feature parameter estimation for kinetic modelinghave not been covered, but interested readers are directed to reportson model-based design of experiments (MBDoE).5143 DeMello and co-workers144 first introduced the concept of self-optimizationof chemical reactions in 2007, which led to further adoption by manyother research groups in the following years. DeMello and co-workersfocused on the synthesis of CdSe quantum dots, but subsequent workshave applied self-optimization to a wide range of synthetic organicreactions including oxidation,145 Diels–Alder,146 methylation,147 Paal–Knorr,148 Suzuki–Miyaura cross-coupling,150 hydrolysis,151 and C–H activation.152Self-optimizationis a modern approach to automating the discoveryof optimal reaction conditions for chemical processes which does notrequire the determination of explicit mechanistic or empirical models.Self-optimization proceeds through iterative cycles of automated reactionexecution, quantification, and algorithmic condition selection toefficiently identify optimal reaction conditions to maximize processmetrics . Although self-optimization wasinitially applied to tuning analytical instruments as early as the1970s,Self-optimization is often conductedusing automated reactors thatcan independently execute reactions at a specified set of reactionconditions. Automated analytical instruments then quantify the individualcomponents of a reaction mixture, followed by an algorithmic suggestionof new reaction conditions based on previous data to improve key reactionoutcomes. Self-optimization research can therefore often be dividedinto three subsections that map directly on to these three key aspects:development of automated reactors, development of automated analyticalmethodologies, and development of optimization algorithms . One or more of these developmentsare typically reported in individual works in the literature. Theseideas are shown conceptually in The primary goals of self-optimization are to reducethe numberof experiments required to optimize a reaction and the experimentalburden on the bench scientist, saving time and money. Additionally,self-optimizing systems offer a more consistent way of generatingdata than human driven reaction optimization. This means that self-optimizationhas the potential to not only accelerate reaction optimization butalso provide data that will enable predictive modeling in the future.Currently, self-optimization has been adopted by both academia andindustry, although work from the former has been published more readily.5.1Automated reactors must be able to receive and execute a set ofreaction conditions without human intervention. Continuous flow andbatch reactors have been applied to self-optimization, and prominentexamples from the literature are herein discussed, but two examplereactor setups are shown in 5.1.1155 Utilizing developments in continuous flow chemistry, these automatedflow reactors use pumps to deliver solutions at a desired flow rateto a temperature-controlled reactor. By pumping solutions of startingmaterials and reagents at varying flow rates, precise stoichiometriesand reaction times can be achieved. Automated flow reactors are attractivebecause a bespoke system can be quickly assembled using commerciallyavailable parts, or a complete system can be purchased from severalspecialized vendors. Building a bespoke system offers flexibilityand lower costs, while complete systems enable faster (and often easier)application deployment.156 An additionaladvantage of automated flow reactors is that the optimal conditionsfound in an automated flow reactor can be utilized for medium-to-largescale production (grams to kilograms per week) by either running thereactor for an extended period157 or numbering-up,158 as discussed in Most self-optimizationstudies in the literature have employed automated flow reactors.159 Third, automated flow reactors suffer fromstandard issues with flow chemistry, particularly reactor cloggingdue to precipitation of solids,160 andlimits in the maximum residence time of compact reactors.161However, three major challenges arefaced by researchers using automated flow reactors for self-optimization.First, automated flow reactors can consume large amounts of startingmaterial and solvent due to the need to flush the reactor when changingconditions. The standard heuristic is to wait at least two residencetimes  prior to initializing analytical measurements,so much of the reaction material is directed to waste. Second, changingcategorical reagent conditions such as catalyst, base, or solventis nontrivial in a standard automated flow reactor because each pumpmust be loaded with a particular reagent prior to reaction execution.162 These reactors employliquid handlers that transfer the individual components for a reactioninto a sample loop prior to injecting them as a droplet into the reactortubing. The droplets can be as small as several hundred microliters,resulting in large reaction material savings when compared with traditionalcontinuous flow experimentation. For example, Jensen and co-workersdemonstrated the self-optimization of a C–N cross-couplingreaction in an automated droplet flow reactor varying catalyst andbase with a liquid handler , although in practice theyhave only been used for reactions with shorter reaction times (minutes).A variety of technologies have been developed toaddress otherstandard issues with flow chemistry. Methods to prevent clogging includecontinuous stirred tank reactors (CSTRs) that can facilitate slurriesOverall, the field of flow chemistry offers a powerful set of toolsfor building automated reactors for self-optimization. By either buildingor buying automated flow reactors, research groups and industry canquickly access the basic laboratory tools needed for building self-optimizingsystems. However, particular care must be taken for solid handlingand the screening of categorical parameters  as vital modifications to existing equipment may be necessary.5.1.2149 Using these liquid handling instruments, it is possible to screenmany categorical variables, such as catalysts, ligands and solvents.Hein and co-workers conducted self-optimization of a stereoselectiveSuzuki coupling reaction using a ChemSpeed liquid handling robot,varying the ligand, several stoichiometries, and temperature to maximizethe formation of the E-product and minimize the Z-product, as shown in 149 This self-optimizationcampaign led to more than a 2-fold increase in yield of the E-product  and a notable increasein the E/Z ratio  within 161 experiments.Recentwork has shown that automated liquid handling robots, historicallydeployed for high-throughput screening (HTS) in biological applications,can also be used in combination with high-throughput screening microplatesto create automated batch reactors for self-optimization.As automated liquid handling robots are commonamong high-throughputexperimentation (HTE) and process development groups in industry,automated batch reactors represent a significant opportunity for industrialadoption of self-optimization. Indeed, the few publicly availableself-optimization studies that have been executed using automatedbatch reactors were from industrial-affiliated groups. HTE is furtherdiscussed, in detail, in 5.1.3154 This trend is likely due to the ubiquity of HPLC and UHPLC instrumentsin chemical synthesis laboratories and the relative ease with whichthey can give quantitative data and be integrated into self-optimizationsystems. For automated flow reactors, HPLC sampling is achieved usinga switching valve located at the outlet of the reactor, which canalternate between directing reaction material to waste during changesbetween conditions and sending aliquots to the HPLC instrument foranalysis; this is referred to as online sampling, as material is removedfrom the flow path for analysis.169 Thissampling is typically initialized based on the residence time of thereaction or, in the case of droplet flow reactors, an in-line UV cell.In addition to HPLC, gas chromatography (GC) has also been reportedin the literature, but this is much less common.152The mostcommon analytical techniques used in self-optimization are chromatographic,either high performance liquid chromatography (HPLC) or ultrahighperformance liquid chromatography (UHPLC).172 Additionally, in-line IR and FTIR has been used independently andin combination with NMR and HPLC for reaction quantification.170 The advantage of in-line analytical techniquesis they offer fast data feedback, enabling more rapid optimization.However, because the analysis is conducted on the crude reaction mixture,these complex mixtures can suffer from overlapping peaks which oftenmakes quantification difficult. The supporting information of thereview by Rincon and co-workers has a list of flow chemistry self-optimizationstudies and the on-line/in-line analytical techniques used.154In addition to online HPLC, there have been manyreports on developing in-line analytical techniques, such as NMR,IR, UV, and FTIR. In-line NMR has been used to identify both knownand novel products synthesized in self-optimizing reactors.5.2173 This functional view of chemicalreactions makes it clear why optimization algorithms, which find theoptimal values of mathematical functions, can be used to optimizechemical processes. Optimization algorithms iteratively evaluate theoutput of the function at different input values until a maximum or,if desired, a minimum, is reached. In the case of chemical processes,these iterative evaluations correspond with intelligently suggestedexperiments to execute in the laboratory until a set of reaction conditionsare achieved that give the optimal desired output.In self-optimization,an efficient optimization algorithm is required to select new reactionconditions based on previous results. Chemical reactions can be viewedas mathematical functions that receive reaction conditions as inputvalues and output reaction outcomes .5.2.1145 and the simplex algorithm,which explores the design space based on geometric transformationsto exploit perceived favorable areas.147 The challengewith local optimization algorithms is their dependence on the reactionconditions used to initialize the algorithm; if there are multipleregions of chemical space with local optima, the algorithm could potentiallyfail to find the best overall reaction conditions for the transformation,as illustrated in The two main classes of optimization algorithms are local and globaloptimization algorithms. Local optimization algorithms are designedto find the optimal values of a function closest to an initial guess.Therefore, if there is one optimal value, local optimization algorithmswill likely find it, but if there are multiple optima, the successof a local optimization method is highly dependent on the initialguess. Example chemical applications of local optimization algorithmsinclude the steepest descent algorithm, which chooses reaction conditionsbased on the most favorable gradient (direction) in design space toexplore,145 They used the Stable Noisy Branchand Fit (SNOBFIT), which, as its name suggests, relies on sequentialbranching and fitting steps. The algorithm begins by subdividing theoptimization domain into boxes with one data point each and subsequently builds full quadratic models for each box and itsnearest neighbors .174 SNOBFITachieves global optimization by sampling evenly from the completereaction condition input space. This has been shown to result in alarger number of experiments than local optimization in exchange fora higher likelihood of finding the optimum for difficult problems.145 SNOBFIT also allows researchers to conduct experimentsin batches .Global optimization algorithms can identify thebest value of afunction independent of the initial guess but may require more experimentsto obtain. Krishnadasan and several other researchers were the firstto apply global optimization to self-optimization.178 This class of optimization algorithm is a subset of Bayesian statistics,which uses probability to express certainty in future outcomes basedon past observations. In the case of reaction optimization, when anexperimental iteration is completed, a probabilistic model is trainedto predict its reaction outcome  given the reaction conditions.Then, the Bayesian optimization algorithm chooses experiments thatbalance further exploration of chemical space and exploitation ofthe known best performing conditions.179 Bayesian optimization offers a principled and efficient way to applyglobal optimization to chemical reactions that is more intuitive andoften more effective than other global optimizers.More recently, researchers have started to applyBayesian optimizationwithin chemistry optimization problems.5.2.2181 The algorithm fits predefinedmathematical models for each combination of categorical variablesand, once the best categorical combination has been identified, itsuggests further experiments to improve the fit of the model. However,this approach requires users to specify a kinetic model a priori,which may be difficult when a full reaction mechanism is not known,as discussed in Each of the aforementioned algorithms only utilize continuous inputvariables , butchemistry problems often have categorical variables also . To address this algorithmic limitation, Jensen presenteda branch-and-fit algorithm that eliminates possible values for categoricalvariables  that lead topoor reaction performance.178 These algorithms tended to perform slightly better in finding optimalreaction conditions than the aforementioned strategies that couldnot learn a relationship between categorical variables and may bea large research area of interest in future. Categorical variablescan be explored more easily by quantifying them with various “continuousvariable” chemical descriptors, as highlighted in 182 showed the effectiveness of their mixed variable multiobjectiveoptimization (MVMOO) algorithm in optimizing categorical/continuousvariables without chemical descriptors. In their respective works,they show the use of novel distance metrics based upon Gower similaritiesthat reduce this necessity in both simulated and experimental casestudies.Alternatively,it is possible to use optimization algorithms that inherently workwith categorical variables. In recent studies, Bayesian optimizationalgorithms were adapted to automatically learn the relationship betweencategorical variables from experimental data.5.2.3150 This constraintwas implemented to prevent the yield being maximized by simply addinghigher loadings of an expensive catalyst. In other cases, researchershave optimized a weighted function of multiple objectives.183 Both of these methods requirethe scientist to make an a priori judgment about the trade-offs betweencompeting objectives and their respective importance. This prior judgmentcan be limiting and results in a single optimum point identified,whereas there are likely multiple optimal solutions depending on theweightings of the individual objectives.Optimizationproblems in chemistry often involve trade-offs between multiple competingobjectives, such as balancing high process yields with low costs,so optimization algorithms need to be able to consider and weightthese objectives to find optimal solutions. For example, Jensen andco-workers optimized catalyst turnover number  with the constraint that yieldmust be greater than 90%.184 These algorithms construct aPareto front, which is defined as a set of points in which an improvementin one objective would result in a detriment to another. By presentinga spectrum of trade-offs, this multiobjective approach allows thescientist to select one optimal point with other softer constraintsconsidered . Due to the need to optimize multiplevariables and explore globally, multiobjective Bayesian optimizationalgorithms can be slower than gradient descent or single objectiveoptimization and require further experimentation.An alternative approachto considering these trade-offs is the use of multiobjective optimizationalgorithms. These algorithms explore the full set of trade-offs betweenmultiple objectives.184 applied to a N-benzylationreaction.175 The flow rate of α-methylbenzylamine 38, ratio of benzyl bromide 39 to 38, solvent flow rate, and temperature were modified to maximize productionof 40, while minimizing formation of 41.After 20 experiments designed by Latin hypercube sampling (LHS),185 a balanced random sampling technique, TSEMOquickly identified 58 further experimental conditions on or near thePareto front. The Pareto front indicated that a 60 kg m–3 h–1 increase in space-time yield (STY) would correspondwith an approximate 10% increase in impurity yield, which the scientistcan then consider based on the needs of the process.5.2.4188The self-optimization reports explored thus far primarily focuson single experimental case studies, implementing bespoke optimizationstrategies; this makes it difficult to compare the performance ofoptimization algorithms objectively. Therefore, recent work has aimedto develop chemically relevant optimization simulations of reactions,or benchmarks, so that these algorithms can be compared without thetime and expense of laboratory experiments.Benchmarking rarely finds algorithms that will work in allsituations, but they can help filter out poorer-performing algorithmsand compare the effect of small changes to algorithms for potentialbenefits. Furthermore, benchmarking studies can act as postverificationof algorithms developed initially on real experiments, as it is possibleto run large numbers of repeated simulations to understand the averagebehavior of these algorithmic techniques.5.3156 Therefore, thereare still open research questions tackling how to develop highly flexibleautomated reactors that can adapt to a wide range of chemistry withoutsignificant customization. There are also further necessities whenconducting reactions that must be addressed, particularly steps thatare easy for humans but more complex for machines, such as phase separations,extractions, crystallizations, etc.Self-optimizationhas the potential to significantly accelerate reaction developmentby enabling autonomous optimizations of reaction conditions. However,currently, as highlighted by Hein and co-workers, automated reactorsoften require significant human intervention and adjustment to achievehigh quality results.190 with one active learning examplefrom Lapkin and co-workers with the focus of pH adjustment.191 There is also a significant opportunity forbenchmarking these algorithms on both in silico and real-life experimentalcase studies to see how well they generalize to all classes of reactions.Additionally, self-optimizationhas shown promise in the automated optimization of single reactions,but there is a wealth of reaction data available that current algorithmsare unable to utilize. Very recent work has used transfer learningtechniques to accelerate optimization by leveraging data from similarreaction optimization campaigns, but this has mainly been demonstratedin silico,66.1200 Traditionally, this process has been employed in the pharmaceuticalindustry for the parallel synthesis/assessment of chemical compoundson a variety of scales. This includes small, focused arrays of compoundsfor exploring structure–activity relationships (SAR) arounda hit compound of interest, and the synthesis of thousands of compoundsto populate high-throughput screening (HTS) libraries. In recent academicsettings, HTE has also shown utility in the discovery of new chemicalreactions, as this technique is well-suited for the discovery of unexpectedreagent combinations enabled by the large number of reactions thatcan be run in tandem.205 For the purposes of this review, this section will focus on theapplication of HTE to accelerate the optimization of synthetic organicreactions. Herein, methods employed for the simultaneous explorationof chemical space using multifactorial optimization will be discussed,touching on the strengths and weaknesses of the different methodscurrently available to HTE practitioners.High-throughputexperimentation (HTE) involves running multiple reactions in parallel,which is a useful technique for quickly exploring chemical space ina systematic and standardized manner.6.1.1192 Commonly a fractionalfactorial approach is employed where a subset of variables are screenedin a matrix array with all categorical variables compared againsteach other. Although this approach can be time-consuming to designand analyze, it is generally more cost-efficient than OFAT  screen may help to identify optimal conditions muchfaster and more efficiently than performing reactions individually. However, reaction optimization using a HTE methodologyrequires time-intensive reaction design from the outset, as severalcategorical variables  may bevaried at once.OFAT see 2 or othe6.1.2Duringthe lead optimization phase of a drug discovery campaign, elaboratedcompounds (leads) produced in the development of a clinical candidatecan be very time and resource intensive to make. High-throughput optimizationtechniques can be useful to optimize potentially intractable reactionssuch as transition-metal catalyzed cross-couplings of highly functionalizeddrug-like compounds, however, these elaborate pharmaceutical intermediatesoften cost more gram-for-gram than precious transition-metal catalyststhat may be employed in their synthesis. By downscaling chemical reactionsinto a miniaturized format, one can increase the number of data pointsgained during a HTE screen without the associated impact on materialcost.206 This miniaturizationapproach not only allows for a greater number of experiments to berun in a material sparing fashion, but is also highly appropriatefor automation. Hence, a number of robotic platforms have been developedby vendors  to facilitate the execution of multiple experiments at oncein a Society for Biomolecular Screening (SBS) footprint microtiterplate (MTP). This data capture workflow was further streamlined withthe advent of analytical systems equipped with autosamplers that cansample directly from the same footprint MTPs. The standardizationof these plates to a universal footprint ensures interchangeabilitybetween different robotic systems, and this has undoubtedly had anenormous impact on the rate with which experiments can be performedand analyzed within the biological sciences.For decades, biological assays have been routinely performedin plate-based formats on micro- or nanoliter scales in a parallelizedhigh-throughput manner.Organic chemistryhas been slow to adopt MTPs as, unlike biologicalexperiments which are run under aqueous conditions at physiologicaltemperature (37 °C), synthetic organic chemistry experimentsemploy a wide range of temperatures  and differing polarity solvents, some ofwhich are incompatible with plastic MTPs. Accordingly, when chemistsperform reactions in parallel, they tend to miniaturize their chemistryfrom round-bottomed flasks (RBFs) to 96-well plates and seldom downscalefurther to 1536-well MTPs. 6.1.3207 or radial208 flow techniques, or (3)microfluidic (and droplet) reactors.204 These platforms have been utilized for a number of different applicationsincluding miniaturized synthesis and reaction discovery, but the examplesshown herein are those which have been applied to reaction optimizationonly.209Platforms used for automated HTE can be broadly characterized intothe following three formats: (1) plate-based reactors where individualreactions are either performed directly in the plate wells or withina glass vial insert, (2) flow-based platforms employing continuous6.1.3.1213 can be weighed out manually with small spatulas or using 3D-printedscoops,210 which can somewhat streamline the meticulousand demanding process. Unlike other nanoscale approaches in 1536 MTP,these 96-well reactors now allow an experimentalist to operate ona scale which is compatible with solid handling of chemical substances.Although the addition of solids to a microscale reactor can be moretime-consuming than the addition of liquids or stock solutions, theability to use solids is important as it broadens the variety of chemicalreactions that can be performed, as not all chemical reagents canbe effectively dispensed as stock solutions due to issues such asheterogeneity and chemical instability.Microscale plate-based chemistry involving 96 individual glassvials housed within a metal block 1 and 2 i215 to streamlinereaction implementation and optimization. The flexibility and accessibilityof this 96-well approach has led to widespread usage with multipleliterature examples for HTE optimization217 of a variety of reaction classes with selected examples shown in Another key benefitof working on microscale in 96-well reaction blocks is that the reactorsthemselves are suitable for a wider range of reactions than can beperformed in plastic MTP reactors, the glass vial inserts in the 96-wellplates exhibit good chemical tolerance akin to the traditional roundbottomed flask, furthermore, they can be heated or irradiated, andthe contents of the vials shaken on an orbital shaker or magneticallystirred with the addition or small magnetic fleas. Although automationis not necessary for reaction implementation using this setup (unlikethe 1536-well MTP approach), the SBS footprint of these reactors meansthat they are compatible with a variety of different platforms forautomated liquid and solid handling6.1.3.2198 is a commonly reported HTE method which canfacilitate the collection of thousands of data points within a multiparameteroptimization.229 Due to the minute volumes of material employedin this approach, access to specialized liquid handling robotics isrequired. Examples include SPT Labtech’s Mosquito positivedisplacement multichannel pipetting system, which can dispense andaspirate volumes in the range of 25 nL to 1.2 μL, or Beckman’sEcho liquid handler, which can dispense volumes in the range of 2.5nL to 5 μL using Echo Acoustic Technology. These platforms areincreasingly used for nanoscale  synthesis of pharmaceuticallyrelevant compounds with examples of also incorporating nanoscale biologicalscreening in a “direct-to-biology” approach.239Nanoscale 1536-well MTP chemistry, recently referred to as ultraHTE,242 These reactions include Suzuki cross-couplings, reductive aminations, N-alkylations, nucleophilic aromatic substitutions (SNAr), etc., as well as transition-metal catalyzed couplingson pharmaceutically relevant molecules such as the Pd-catalyzed Buchwald–Hartwigamination, metallophotoredox C–N and C–O bond formations,and C–H functionalizations to furnish C(sp2)–C(sp3) bonds  bonds 4.6.1.3.3249 In the past decade, several ground-breakingdevelopments in the analytical sciences have occurred which now permitthe ultrafast analysis of high-throughput reaction screening at increasinglyimpressive speeds. For example, techniques like matrix-assisted laserdesorption/ionization (MALDI),251 desorption electrospray ionization (DESI),254 and acoustic ejection MS (AE-MS)257 have been reported for the high-throughput data acquisition of 1536reactions in under 10 min and requiring only nanolitre volumes ofcrude reaction mixtures. These techniques are rapid, and in some casesthe analysis can be performed directly from a 1536-well MTP, however,the equipment required can be expensive. Other options for rapid analysiswhich can be performed on standard UHPLC hardware interfaced witha single-quadrupole mass spectrometer is the Multiple Injections ina Single Experimental Run (MISER) technique developed by Merck.258 This flow-injection analytical method injects multiple samples back-to-backwith limited or no chromatographic separation and uses single-ionmonitoring (SIM) to detect analytes. With inexpensive UHPLC-MS equipment,run times are reported to be as low as 10 s per sample, resultingin the data acquisition of 1536 samples possible in around six hours.The main drawback of this technique compared to the ultrafast MS methodsis that reformatting from a 1536-well MTP to four 384-well MTPs  speedof acquisition, (2) sensitivity of the technique employed, (3) theamount of structural information gained from the technique, and (4)the level of quantification that can be achieved. With ultrafast techniqueslike MALDI, DESI, AE-MS, etc., quantitative data can be acquired providingan internal standard is added to the analytical samples prior to analysis,an authentic sample of the analytes can be obtained, and a calibrationcurve run to compare measured analyte response and concentration tohenceforth determine yield. This calibration is necessary as ionizationresponses of different analytes can be nonlinear and difficult tocompare reliably. However, even if authentic samples of the analytescan be sought, other phenomena like ion suppression can add additionalcomplications to the level of accuracy of yield assessment that canbe realistically achieved with MS-based techniques. More conventionalLC-MS methods which chromatographically separate analytes and useUV absorbance  to determine analyte concentrationtend not to suffer from the same issues as MS-based techniques .However, these methods require longer run times (cf MISER) and mayrequire optimization of the elution gradients to ensure peak separation.Thus, although this technique can afford increased confidence in thelevel of quantitation that can be obtained, this comes at the costof prolonged analysis times for both acquisition of data and deconvolutionof reaction outcome 15.6.1.3.4260 however, before this can beaccomplished, reoptimization is often required as chemistry seldomtranslates directly from batch or plate to flow.230Flowchemistry is a technique that is highly applicable for optimizationmethods such as self-optimization, as discussed in 207 The autosampler which would normally house analytical samples isrepurposed to hold up to 192 source vials containing stock solutionsof reagents, which is a significant increase in capacity comparedto existing commercial platforms. The reaction segment is assembledin ∼45 s, where 1 μL of each reagent is aspirated andthen the whole slug is injected into a flowing solvent stream anddelivered into the reactor with approximately one-minute residencetime. The reaction mixture is then directed into a 96-well plate fractioncollector and subsequently analyzed via LCMS. This system has thebenefit of not only containing several switching valves which canpermit selection of a several solvents but can also direct the reactionslug to one of two LCMS machines for analysis. This platform enabledthe execution and analysis of around 1500 reactions in a 24 h periodand was also subsequently employed for the optimization of a photoredoxcatalyzed decarboxylative minisci-type C–H arylation to affordbicyclo[1.1.1]pentane containing compounds, which are medicinallyrelevant and traditionally challenging to synthesize.261Pfizer has recently reported a bespoke flow platform for nanoscaleHTE based on modified HPLC equipment, as shown in 262 This work utilizes “stopped-flow”experiments and machine learning models to map chemical reactivityand synthesize diversity-oriented libraries. This led to a systemthat can predict optimal synthesis conditions with 92% accuracy anda dramatic increase in the success rate of initial library screensfor reactivity while achieving a 90% reduction in reagent consumptionwhen compared with continuous flow.A workflow recently reported by Bourne and co-workersalso showsthe use of a bespoke flow reactor platform for library synthesis andoptimization.6.1.4Datadriven optimization involves the use and analysis of relevant datato guide decision making in pursuit of the global optima for a givenprocess. In practice, the success of this endeavor is highly dependenton the quality of data utilized, a factor that is increasingly importantfor autonomous reaction optimization systems using algorithmic, generative,and machine-learning (ML) derived models. One of the biggest challengescurrently faced in this area is the acquisition of abundant and high-qualitydata sets. The availability of reaction data accessible to cheminformaticianshas dramatically increased as online databases such as SciFinder-CAS,Reaxys, and the United States Patent and Trademarks Office (USPTO)have become accessible, as discussed in 263Although data sets compiled from miningpatent repositories and published reaction literature are extensiveand cover a wide-ranging number of reaction classes, there are limitationsto the quality of data as it is merged from many different laboratoriesusing different methods and different equipment for quantification.Deviations in reaction outcomes of the same reaction might arise fromhuman error in the laboratory, transcription, or limitations of thetext mining software, varying analytical methods and different reactionscales . Moreover, it must be noted that generallyliterature data is biased toward higher yields as low yielding outcomesare often not reported. In detail, NMR yields are usually calibratedto an internal standard within the reaction sample or probe, althoughisolated yields are considered the “gold standard,”these can be limited by sample recovery success which is highly dependenton the purification techniques used. UV-  or mass-based techniques such as HPLC-MS or GC-MS can alsogive varying degrees of quantification as UV of MS responses can varybetween compounds and purely quantitative results are only achievedby comparing the mass or UV response to an analytical calibrationcurve. By contrast, HTE offers an opportunity to generate data witha high degree of standardization, reactions tend to be performed andanalyzed on the same scale with the same equipment using the samestock solutions. The power of this approach is apparent when comparingthe variance in yield prediction models trained on HTE-derived datafor specific reactions compared to a much larger data set from USPTOas highlighted by Reymond and co-workers.6.1.5The past decade hasseen a shift in the application of HTE from the generation of chemicalsamples for HTS libraries to conditions screening and optimizationof chemical reactions by taking advantage of the ability of HTE torapidly survey a wide area of chemical space using a small amountof material. Recent advances in HTE and the translation of nanoliterrobotics like the SPT Mosquito and Beckman Echo, originally designedfor biological assays and now being applied to parallelize organicsynthesis, has fueled a rise in reports of ultraHTE where hundredsor thousands of chemical reactions can be executed in parallel ona miniaturized scale. Although limitations still exist with regardsto the types of chemistry that are amenable to these plate-based formats,further development to increase the flexibility and generality ofchemistry which can be performed in this fashion will be a benefitto the field of reaction optimization by HTE. Furthermore, as HTEplates are sealed and heated as a block, flexibility in reaction times,reagent equivalents, and temperature controls are limited as the entireplate is subjected to the same conditions. This means that althoughpromising categorical variables can be identified as “hits”for promising potential optimal conditions in reaction optimization,many continuous variables must then be optimized upon scale-up tomore traditional batch or flow laboratory scales.As discussedherein, another advantage of translating organic chemistry to a miniaturizedHTE configuration is the ability to rapidly generate large amountsof standardized data in a relatively short time frame creating informationdensity for a desired reaction or class of substrates. Currently,there are few examples of utilizing HTE-generated data to train MLmodels and the increased availability of more publicly available datasets would be particularly useful to the machine learning in the chemistrycommunity. In this regard, challenges exist around the extent of quantitativedata which can be obtained using an ultraHTE approach; currently thereis a trade-off between speed and quantitation, and there are alsoconstraints around the number of different analytes that can be analyzedin a quantitative manner in tandem. Recently, there has been significantprogress in the field of analytical chemistry and robotics, whichhave directly facilitated the renaissance of HTE in the field of organicsynthesis and further developments are sure to modernize the areafurther and increase the number of data sets available to data scientists.6.2264 natural language processing,265 and autonomousdriving.266 Within the field of organicchemistry, ML also represents an emerging tool, particularly for predictiontasks such as retrosynthesis, optimal reaction conditions, or reactionoutcomes. It is then also possible to use these predictions from MLto influence starting points and process bounds for real-world optimizations,whether in self-optimization, HTE, or otherwise.Machine learning (ML) has already revolutionizedvarious areas, such as image recognition,The predictionof reaction outcomes at specific reaction conditions or direct predictionof reaction conditions are relevant and particularly attractive forreaction optimization. As shown in 6.2.1Moleculesmust be translated to a machine-readable, typically numerical, formatthat can be used as an input for ML models, prior to their use. Werefer to this translation process as molecular parametrization asit aims to capture relevant molecular properties for a particularreaction. For different chemical transformations, different propertiescan influence reaction outcomes such as steric hindrance of a functionalgroup or electronegativity of neighboring atoms. Moreover, the parametrizationstrategy should also be chosen to allow optimal compatibility withthe ML model used, as prediction performance will depend on the compatibilitybetween input format and ML model.229The baseline parametrizationmethod for representing chemical inputs is one-hot encoding (OHE).A one (1) or a zero (0) represent the presence or absence of specifiedreaction components respectively: no chemical information is encoded.This approach has been shown to be effective for a variety of chemicaltasks, including yield prediction, but cannot extrapolate to new partsof chemical space.267 One of the advantages of these fingerprintbased methods is that they are considered cheap features for modeling;their generation does not require a vast amount of computing power/time.Yet, their ability to explicitly capture molecular properties  of molecules is limited. Typically, models thatuse fingerprints develop knowledge in an indirect manner, such asan implicit understanding of electronegativity associated with differenthalides, for example.268Extended-connectivityfingerprints (ECFP) is a parametrizationmethod that captures atom types, neighboring connectivity relationships,bond types, and represents the outcome in a machine-readable one-dimensionalbit-vector. Circular fingerprints  aregenerated by (1) assigning identifiers to each atom in the molecule,(2) updating each atom’s identifiers depending on the neighboringatoms, (3) removing duplicates, and (4) compressing the data intoa vector of set length, e.g., 1024 bit (a number of zeros and ones).229 As a result, DFT can be used to calculate descriptorsthat quantify the specific chemical properties of the given set ofligands such as the bulkiness of a molecule or electronegativity ofatoms within a molecule.269 However, DFTcalculations for large libraries are often more time-consuming thanactually running the corresponding reactions in a high-throughputscreening format.A much morecomprehensive parametrization approach is calculatingmolecular descriptors using density functional theory (DFT). DFT canbe used to determine the ground/excited state of molecules and thusoffer fundamental insights into geometric and electronic properties.270 where results can be achieved by training amodel to predict the next word in a sentence across a wide varietyof texts. Because chemistry can be represented as a language in theform of simplified molecular-input line-entry system (SMILES),271 a language model can be trained to predictthe next atom in a molecule when given only a portion of the molecule,thereby saving computational expense.272 Because the model must understand a significant volume of chemistryto be able to predict a SMILES string, its numerical output can beused as a “learned fingerprint” for other predictiontasks.273 Furthermore, the learned fingerprintcan be tuned for each downstream task such as yield prediction usingstandard neural network training.More recently, parametrization work has utilizedneural networksto achieve the tailored nature of DFT descriptors without the computationalexpense. This work is divided into two approaches: natural languageprocessing models and graph neural networks. The former leveragesrecent advances in language models such as transformers,275 MPNNs have been extended togenerate fingerprints for reactions, with state of the art results.276 An overview of these techniques is shown in Alternatively, graph neuralnetworks represent a molecule as aninterconnected network of atoms and bonds. These networks can be trainedto produce a “learned fingerprint” for prediction tasks.One of the most widely used forms of graph neural networks in chemistryare message passing neural networks (MPNNs), which learn relationshipsbetween neighboring atoms through iterative “messages”passed along bonds.6.2.2243 Subsequently, Hirst and co-workerscontinued the work by Ahneman by using another machine learning technique,support vector machines (SVM), in which they demonstrated improvedprediction performance.277 Glorius andco-workers successfully boosted this prediction performance usinga concatenation of fingerprints.278 Doyleand co-workers then used a combination of fingerprint based and DFTbased descriptors for the prediction of reaction performance for adeoxyfluorination HTE data set, thereby guiding the search towardhigh yielding conditions.279 Overall, dueto the consistency of HTE generated data sets, good results couldbe achieved with regard to the predictive performance of the appliedML models.Reaction outcome prediction hasprimarily been carried out on data obtained via HTE or similar techniquesfor generating consistent data sets. Doyle and co-workers traineda random forest (RF) algorithm on HTE data of a Buchwald–Hartwigreaction, aiming to generate an automatic feature generation algorithm.They demonstrated success using their approach, which was trainedon 5% of data and outperformed linear regression trained on 70% ofdata.6.2.3281 Additionally, Pistachio represents a commercially available dataset, based on USPTO data, electronic lab notebook (ELN) data, andinformation obtained from journals or other patent literature containing13.3 million reactions.282 Most recently,the open reaction database was created to build a standard formatand open-access location for reaction data, which represents a largeshift in fair data accessibility.283284While most ofthis covered literature used data solely generated via HTE or flowchemical platforms for training ML models to prediction chemical reactivity,an increasing effort is made in developing predictive models withchemical data mined from online databases. Reaxys, a commercial databaseby Elsevier, and the United States Patent and Trademark Office (USPTO)reaction database are the two most used sources for data-mined applications.The Reaxys database is proprietary and contains information on morethan 56 million reactions from over 16 000 journals. In contrast,the USPTO database is accessible to the public and contains chemicalreactivity information obtained from over nine million patent applications.6.2.4287To build predictivemodels based on reaction databases, one of the most significant challengesis extracting data into a standard format amenable to machine learning.The database providers mentioned perform some amount of preprocessingto extract the data into a common format. However, the databases oftendo not capture important details of the procedure such as the reactiontemperature, workup protocol, or analytical results. Furthermore,the same reagent can be represented in different ways across the database. To overcome this issue with data quality, recentwork has developed deep learning models that can extract text descriptionsof synthetic procedures in a standard format.288 This is often caused byscientists using the same procedure for a standard reaction as isreported in the literature. Therefore, a filtering process often includesremoving duplicate reactions, discarding reactions with missing keyreactants or reagents , and excluding reactions withouta numerical yield. This filtering process can often result in lessthan the 30% of the original extracted data being utilized for machinelearning. For example, Reymond and co-workers created a data set ofBuchwald couplings based on data extracted from several databasesand, after filtering, only 15% of the original reaction records remained.289Subsequently, the data must then be filtered, in particular,as noted by Varnek and co-workers, there are many duplicate reactionsin databases.6.2.5290 Their neural network architecture was designed to reflecta chemist’s intuition. Often, catalysts are selected followedby solvents, reagents, and temperature, so Jensen and co-workers’snetwork first predicted catalysts and then conditioned each furtherprediction  on the prior ones.On unseen reactions, the neural network’s top-3 predictionsincluded those used in the literature with 50% accuracy. Furthermore,the correct catalyst was selected with over 93% accuracy.Upon data set extraction from the literature,machine learning models can then be used to predict reaction conditionsdirectly given a set of reactants. The first examples of such a modelwere developed by Jensen and co-workers, who used a feed forward neuralnetwork to directly predict reaction conditions given the differencein the ECFP fingerprints of the products and reactants, as shown in 291Approaches that directly predict reaction conditionsdo not accountfor the yield of the reaction; instead, these techniques aim to predictthe conditions used most often in the literature. The downside ofthis approach is that optimal reaction conditions for a particulartransformation often require going beyond the standard conditionsused for a transformation. Therefore, reaction condition predictionmodels might be used to give suggestions of starting points for furtheroptimization rather than predicting optimal conditions directly.292 to custom neural network architectures.293 Furthermore, machine learning models can beused for retrosynthesis, which has been reviewed extensively elsewhere.294In addition to predicting reaction conditions,machine learningmodels can be trained to predict the likely products of a reactiongiven the reactants. A variety of models have been trained for thispurpose, ranging from natural language processing models 6.2.6295 These ML models can therefore be combined withthe optimization techniques described in 15Thus far, MLhas been applied to yield prediction and reaction condition prediction,both of which have potential use for reaction optimization. Scientistscan use yield prediction models to narrow the potential optimal setsof conditions for a reaction without brute-force screening. Recentwork by Grzybowski, Burke, and co-workers showed this by highlightingthe use of ML and closed-loop optimization to identify general high-performingreaction conditions for a Suzuki–Miyaura coupling.263 Therefore,more high-quality data sets with reaction outcomes recorded and furthermodel development are needed to create general ML models for reactionoptimization. If successful, this research could be transformativein reaction optimization and transition the field to more direct predictionsof optimal reaction conditions. For a recent review of in-depth modelingof HTE data sets, also refer to Jensen and co-workers.296Currently, ML models have been very successful inreaction outcome prediction on HTE data sets for a single reactantpair. We foresee that leveraging a broad selection of data from targetedexperiments could aid general prediction of key reaction outcomesfor specific classes of reactions. Reymond and co-workers attemptedto build a general yield prediction model based on the USPTO dataset, but their model has low predictive quality due to the sparsenature of the USPTO yield data.7297 Theability to deliver commercially relevant quantities of product, withan acceptable investment, operational and environmental cost, as wellas process safety, are the focus of this stage of process design.Furthermore, continuous fulfilment of quality critical parametersis investigated at this step, fundamental for pharmaceutical and finechemicals industries.298 Development ofprocesses meeting these criteria is defined as scale-up and involvesdetermination of the critical scale-dependent factors that would affectthe choices of the most functional reactor architecture, process conditions,and separations steps. These are the next steps from bench-scale reactionoptimization and understanding how previously obtained optimal conditionsmay change is crucial to the successful scale-up of chemical processes.Lab-scalereaction optimization studies focus on improvements inreaction yield and purity, optimization of reaction cost and greenness,and development of the optimal workup/separation approach, whereassubsequent process design steps  need to addressa different set of requirements.299 Therefore, it is often very difficult to reproduceat scale exactly the same conditions obtained when using small-scaleequipment and the most promising operating conditions determined duringlab-scale optimization studies do not necessarily yield an optimallarge-scale process. As a result, many initially promising discoveriesand reaction routes may not achieve broad deployment in industrialproduction.301 While large companies can investvast resources into solving scale-up related challenges,302 smaller research organizations such as start-upsor academia might not be in place to allocate such resources, limitingthe technology-readiness level of developed reactions.297Scale-up implies the attainment of significantly larger productthroughputs compared to laboratory reaction discovery or reactiondevelopment studies. This is traditionally realized by deploying significantlylarger reactors with very different gradients of temperature, pressure,and reactant concentrations to the small-scale processes.305 this section discusses scale-up on a conceptuallevel, providing insight for chemists working on molecular discoveryand benchtop optimization. We anticipate that the consideration ofscale-up challenges and complexity in the early stages of processoptimization can help to guide the laboratory studies toward the achievementof metrics meaningful for large-scale plants and consequently acceleratethe process of launching new molecules and products to market.Determining the optimal reaction conditionsfor the scaled-up reactioninvolves careful consideration of how the processing conditions changein space and time and how these changes relate to the time scale ofthe molecular events driving the reaction. Elucidating these phenomenarequires simultaneous insights into the kinetics, heat transfer, andmass transfer happening inside of the reactor. In contrast to exhaustivescale-up guidelines formulated for chemical engineers and processchemist experts,The following subsections briefly discuss scale-up considerationsto be addressed during benchtop experiments, as well as which phenomenachange during the transition to larger reactors and how to quantifythese changes. Furthermore, we introduce two strategies used to developlarge-volume processes: scale-up and numbering-up, as well as guidethe user toward a choice of applicable equipment for each case.7.1306303 for fast reactions the extended time necessary to mix phasesin a large-reactor results in limited chemical availability of eachreactant leading to suboptimal outputs.In most chemical applications, the design of a large-throughputprocess involves a multifold increase in the size of the reactor vesselsthat were used for kinetic or optimization studies in the laboratoryenvironment. The processing environment is likely to drastically change;this is because of significant reductions in surface area/volume ratiosthat imposes limitations on heat transfer rate, sensitivity to mixing,and different time of addition and removal of products.308 and the ability to generate such models with automatedflow methods has been highlighted as a potential paradigm shift inthe process systems engineering community.162 Notably, such models should be obtained with no mass/heat/mixinglimitations. To design kinetic determination experiments unhinderedby these effects, Jensen and co-workers309 provided a simplified chart-based method for the evaluation of mixingand dispersion in small-scale flow systems, while detailed insightsinto overcoming heat transfer limitation during optimization in flowreactors were discussed by Mase and co-workers.310Therefore, finding the optimal reaction conditionsfor a scaled-upprocess cannot rely on a simple linear increase of the reactant feedand processing time from the optimal lab benchmark. From a processdesign perspective, the availability of the intrinsic kinetic modelcan significantly accelerate process development,303 recommend determination frombenchtop optimization, at a minimum, the following information: (i)reaction network and the significant byproducts, (ii) if the reactionkinetics follow simple power-laws, (iii) sensitivity of the reactionselectivity to mass transfer effects, and (iv) heat evolution andthe potential for reaction runaway. Practical guidelines toward theuse of flow chemistry setup for heat measurements were proposed byMeier and co-workers,311 while Bourne andco-workers312 discussed best practicestoward continuous-flow aided kinetic analysis. Other, case-specificstrategies for extracting meaningful information for process scale-upfrom optimization studies are summarized in the reviews of industrialpractices in large-scale deployment of novel reactive routes,313 such as photoredox catalysis,315 electrochemistry,316 C–H activation,317 reductive coupling,318 and flow chemistry.319Although kinetic models are a powerful tool for acceleratingprocessscale-up, in some cases, the experimental effort necessary to derivethese models is extensive, making it infeasible to realize in thefast-paced process development environment. In such cases, Stitt andSimmons320 In a batch process, feedstocks are suppliedall at once to the reactor, and the treatment of the subsequent loadof feed materials starts only when the previous batch is fully processedand removed. Batch processing is frequently preferred due to its simplicityand is particularly convenient in pharmaceutical processes that requirefrequent cleaning of used equipment, or even removal and disposalof single-use reactors between batches . However, the design simplicity comes at anextra cost of slow processing, increased energy consumption for start-upand shutdown of each batch, significant environmental impact fromcleaning solvents, and more challenging process parameters control.Continuous processing, in contrast, allows for a significant costand environmental footprint reduction (if solvents are recycled323) by faster conversion and increased productivity, reduced down-time,and improved quality by facilitating continuous monitoring of criticalparameters. From the perspective of the chemist, it is important tobe aware of the preferred processing mode and consider process limitationsrelated to each mode within the design of lab-scale experiments.Anotheraspect to address within optimization studies is the preferredproduction mode for the scaled-up process, as the rationale for itschoice may differ between early benchtop experiments and large-scaleproduction, as broadly described by Trout and co-workers.324 describehow to systematically include quality considerations across all phases,including reaction optimization.Within the pharmaceutical and fine chemical industries, the abilityto fulfill quality-critical parameters is one of the key goals ofprocess development, but these aspects are typically not investigatedbefore moving toward scale-up studies. Most recent industrial practices,described by Tsai and co-workers,7.1.1325 whereas a review by Patterson326 addressesthe challenges in modeling of mixing- and temperature-sensitive chemicalreactions at larger scales. Other reports from the literature alsoprovide insightful examples of computational fluid dynamics (CFD)use for modeling of mixing phenomena.328After transferring the data from benchtop optimization to processdevelopment, models of scaled-up reactors are obtained to accountfor engineering phenomena, e.g., characterization of heat and masstransfer environments. If detailed kinetic models are available, theengineer can simulate the operation of a large-scale process and determinea new optimal set of conditions. Gatica and co-workers provided adetailed overview of the industrial practices for the developmentof design equations to model such processes,330 and Wildand co-workers.331 These equations enablean engineer to disregard the parameters that are not critical forthe given reaction, and further develop the nonlinear relationshipsguiding scale-up. If necessary, experiments to determine the significanceof these variables for successful scale-up can be designed. The experimentscan be planned for a partial similarity or complete similarity approach, and advantages and limitationsof both approaches are described by Kind and co-workers332 and illustrated by the case study of a scale-upof competitive chemical reactions.Scale-upengineers seek to quantify the influence of heat and mass transfer,as well as other physical properties, on reaction progression. Thisalso includes the interactions between each physicochemical property.This problem is frequently simplified by dimensional analysis thatenables scientists to develop relations among physical quantities using their dimensionsexpressed in base units .Detailed instructions on the derivation and use of dimensional analysisequations, followed by practical examples of calculations for stirred-tankreactors, were provided by Zlokarnik333 yields a widevariety of novel reactors offering designs that can drastically enhanceheat and mass transfer as well as increase the controllability ofreactants and product spatial concentrations distributions. The choiceof the reactor also includes insights into construction materialsand their interactions with the reaction medium, compliance with theregulatory requirements for the given final product, and the leadmanufacturing time. Guidelines provided by Moran and Henkel334 compare the reactors typically used in industry;the toolbox proposed by Roberge and co-workers includes both the choiceof processing mode and the reactor,335 whilethe contribution of Lindeque336 specificallyaddresses reactor considerations in biocatalytic production of pharmaceuticalscompounds; Dautzenberg and Mukherjee337 discuss the choice and deployment of multifunctional reactors; Westand co-workers338 focus on scalable autothermalreactors. Methods for selection between a wider range of novel, processintensification reactors were proposed by Commenge and Falk,306 and an overview of different options was presentedin an open-source database published by Gorak and co-workers.339Another aspect addressedat the scale-up stage is the choice ofa reactor. While stirred-tank or tubular reactors are by far the mostdeployed reactors in the industrial setting, development of the processintensification field340 Examples of scale-up toward a pilot/manufacturingplant capacity are available in the literature, including a hydrazinecondensation study by Lane and co-workers and the edaravone synthesisby Sun and co-workers.342Selected reactor architectures and theoptimal conditions determinedfor a scaled-up process are frequently verified on a scale of pilotor mini-plant operation, whereas operability and stability are testedin pilot plants. Industrial practice proves that this intermediatestep in process development has the substantial potential to improveprocess understanding and consequently product quality and operationalsafety, however, it also increases time-to-market and overall projectcosts. Design of such plants was described in detail by Whalley.7.1.2158 This enables one to achieve exactly the sameconditions as found in the lab, improves safety by better temperaturecontrol and drastically reduces scale-up time. Possibilities of theaccelerated process development by numbering-up of microreactors arecovered in detail by Kockmann and co-workers343 and Roberge and co-workers.344 However,numbering-up is associated with high investment cost: numerous reactorsare required instead of a single stirred-tank reactor, along withmultiple process control devices.The discussed strategies for process scale-up include both extensiveexperimental insights on laboratory scale, simultaneous modeling ofnumerous phenomena, and potential pilot plant testing. Despite theavailability of the methodological tools described above, it stillmay not be possible to reproduce the selectivity achieved in benchtoptests in the scale-up setting, particularly if the optimization studieswere conducted in microreactors that offer excellent heat and masstransfer characteristics. In such cases, increases in the plant throughputcan be achieved instead by the numbering-up approach, which involvesthe simultaneous use of hundreds to thousands of reactors of the sameor similar scale to the ones used in process optimization studies.345 and the potential to intensify the processcan be evaluated by the process intensification score proposed byvan der Meer and co-workers.347 Capital costs considerationsmean that this approach can be economically justified only for high-endproducts, and for cases where a drastic improvement of output  has been demonstrated. Interestingly, an analysisconducted by the Process Development Team from Lonza348 reveals that 50% of reactions in the fine chemical/pharmaceuticalindustry could benefit from the deployment of microreactor technology.Lowe and co-workers349 proposed a benchmarkingmethod allowing for a similar evaluation across different industries.Notably, as the economic factors are limiting the deployment of microreactors,their market availability is also lower, and the extended lead timecould be a key limitation for decision makers that require fast andlarge product delivery. By more frequent adoption of novel processoptimization methods that involve the use of microreactors, we anticipatethe unveiling of more case studies where excellent optima are achievablesolely by this method, and hence, we can expect accelerated adoptionof more efficient process intensification technologies.Detailed insights into theeconomics of numbering-up were described by Weber and Snowden-Swan,7.2350 It is importantto consider these physical limitations when running optimization campaigns,as algorithms or experimental designs may suggest experiments thatare out of safe operating bounds: this includes reactions that aresuggested at unsafe temperatures or concentrations.For both scaling-and numbering-up approaches, in batch or continuous mode, there areseveral safety concerns to be addressed. Large volumes of flammablesolvent are almost always required, meaning that precautions mustbe taken to minimize the risk of fire from static electricity buildup or exposed flame. However, the greatest concern with organic processesis thermal runaway.351 The exotherms of reactionsneed to be understood via calorimetry studies in the lab, so thatcontrol strategies can be implemented during scale-up.Thermalrunaway occurs when the rate of an exothermic reaction is acceleratedby increases in temperature. In the worst case, this rate accelerationcan lead to secondary decomposition reactions that are more energeticand hazardous than the primary synthesis. While reactions commonlyused in fine chemicals such as the Suzuki–Miyaura cross couplingare often not thought to be dangerously exothermic at the bench, theycan have significant exotherms that could lead to thermal runawayat scale.Tadia) and the maximum temperature of synthesis reaction (MTSR). ΔTadia is the temperature rise in a specific volumefor a specific reaction when all the heat of reaction is deliveredto an adiabatic system . MTSR is defined as the maximum temperaturean adiabatic reactor would reach if cooling failed. In other words,MSTR is the sum of the reaction temperature Tp and ΔTadia. Together, ΔTadia and MTSR can give insights of the worst-casescenario for a particular reaction and reactor. For example, Wangand co-workers conducted calorimetry studies of copper-mediated fluorinationsof bromopyridines and found that the MTSR was above the decompositionof the chosen reaction solvent, DMSO.352 Because decomposing DMSO is an explosion hazard,354 control strategies such as reduced speed dosing or switching toan alternative solvent needed to be implemented.Reactioncalorimetry enables the measurement of the heat producedby a reaction over time. Often, two parameters are determined: theadiabatic temperature rise , and there aretrade-offs to consider for research organizations when deciding whichto implement. These decisions must balance the costs associated witheach technique with the deliverables that they hope to achieve and their associatedaccuracy and reliability. As automated equipment is becoming moreubiquitous and user-friendly, this is one possible solution to unifyseveral fields to conduct reaction optimization techniques in novel, systematic waysto maximize process outputs. This could be in conducting automatedDoE or kinetic studies, screening every combination of reaction variablesin HTE or utilizing self-optimization, or more!In this review, we haveoutlined several modern techniques thatare utilized for chemical reaction optimization to serve as an accessiblereference for interested bench scientists. We have also given discussionson their relationship to further stages of process development, namelyscale-up. There are many distinct methodologies that can be used toobtain optimal reactions conditions for desired outcomes  rather than complete optimizationof their processes. However, as modern laboratories are becoming morediversified in skillsets and more interdisciplinary research is conducted,familiarity with these techniques must be embraced and undergraduate/postgraduatecourses will undoubtedly reflect this more in coming years. It iseasy to envision an evolving chemistry course with practical modulesin DoE, HTE, and more, as the skillsets of chemists diversify beyondtraditional synthesis to meet the needs of the modern laboratory.It is the hope that this timely review will prove the accessibilityof these optimization techniques and help to encourage inspired chemiststo incorporate them into their workflows.