Evolution of catalysts directed by genetic algorithms in a plug-based microfluidic device tested with oxidation of methane by oxygen

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

On-Chip Photonic Detection Techniques for Non-Invasive In Situ Characterizations at the Microfluidic Scale

Microfluidics has emerged as a robust technology for diverse applications, ranging from bio-medical diagnostics to chemical analysis. Among the different characterization techniques that can be used to analyze samples at the microfluidic scale, the coupling of photonic detection techniques and on-chip configurations is particularly advantageous due to its non-invasive nature, which permits sensitive, real-time, high throughput, and rapid analyses, taking advantage of the microfluidic special environments and reduced sample volumes. Putting a special emphasis on integrated detection schemes, this review article explores the most relevant advances in the on-chip implementation of UV-vis, near-infrared, terahertz, and X-ray-based techniques for different characterizations, ranging from punctual spectroscopic or scattering-based measurements to different types of mapping/imaging. The principles of the techniques and their interest are discussed through their application to different systems.

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based modes, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

A comprehensive review of flow chemistry techniques tailored to the flavours and fragrances industries

Due to their intrinsic physical properties, which includes being able to perform as volatile liquids at room and biological temperatures, fragrance ingredients/intermediates make ideal candidates for continuous-flow manufacturing. This review highlights the potential crossover between a multibillion dollar industry and the flourishing sub-field of flow chemistry evolving within the discipline of organic synthesis. This is illustrated through selected examples of industrially important transformations specific to the fragrances and flavours industry and by highlighting the advantages of conducting these transformations by using a flow approach. This review is designed to be a compendium of techniques and apparatus already published in the chemical and engineering literature which would constitute a known solution or inspiration for commonly encountered procedures in the manufacture of fragrance and flavour chemicals.

Reaction condition optimization for non-oxidative conversion of methane using artificial intelligence

Using machine learning and metaheuristic optimization, we optimize the reaction conditions for non-oxidative conversion of methane.

Rapid Prostate Cancer Noninvasive Biomarker Screening Using Segmented Flow Mass Spectrometry-Based Untargeted Metabolomics

Spectrometric methods with rapid biomarker detection capacity through untargeted metabolomics are becoming essential in the clinical cancer research. Liquid chromatography-mass spectrometry (LC-MS) is a rapidly developing metabolomic-based biomarker technique due to its high sensitivity, reproducibility, and separation efficiency. However, its translation to clinical diagnostics is often limited due to long data acquisition times (∼20 min/sample) and laborious sample extraction procedures when employed for large-scale metabolomics studies. Here, we developed a segmented flow approach coupled with high-resolution mass spectrometry (SF-HRMS) for untargeted metabolomics, which has the capability to acquire data in less than 1.5 min/sample with robustness and reproducibility relative to LC-HRMS. The SF-HRMS results demonstrate the capability for screening metabolite-based urinary biomarkers associated with prostate cancer (PCa). The study shows that SF-HRMS-based global metabolomics has the potential to evolve into a rapid biomarker screening tool for clinical research.

Flow Chemistry in Contemporary Chemical Sciences: A Real Variety of Its Applications

Flow chemistry is an area of contemporary chemistry exploiting the hydrodynamic conditions of flowing liquids to provide particular environments for chemical reactions. These particular conditions of enhanced and strictly regulated transport of reagents, improved interface contacts, intensification of heat transfer, and safe operation with hazardous chemicals can be utilized in chemical synthesis, both for mechanization and automation of analytical procedures, and for the investigation of the kinetics of ultrafast reactions. Such methods are developed for more than half a century. In the field of chemical synthesis, they are used mostly in pharmaceutical chemistry for efficient syntheses of small amounts of active substances. In analytical chemistry, flow measuring systems are designed for environmental applications and industrial monitoring, as well as medical and pharmaceutical analysis, providing essential enhancement of the yield of analyses and precision of analytical determinations. The main concept of this review is to show the overlapping of development trends in the design of instrumentation and various ways of the utilization of specificity of chemical operations under flow conditions, especially for synthetic and analytical purposes, with a simultaneous presentation of the still rather limited correspondence between these two main areas of flow chemistry.

Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening

Through iterative selection of maximally informative experiments, active learning renders exhaustive screening obsolete. Chosen experiments are used to train models that are accurate over the entire domain, thus reducing the experiment burden.

Designing Microflowreactors for Photocatalysis Using Sonochemistry: A Systematic Review Article

Use of sonication for designing and fabricating reactors, especially the deposition of catalysts inside a microreactor, is a modern approach. There are many reports that prove that a microreactor is a better setup compared with batch reactors for carrying out catalytic reactions. Microreactors have better energy efficiency, reaction rate, safety, a much finer degree of process control, better molecular diffusion, and heat-transfer properties compared with the conventional batch reactor. The use of microreactors for photocatalytic reactions is also being considered to be the appropriate reactor configuration because of its improved irradiation profile, better light penetration through the entire reactor depth, and higher spatial illumination homogeneity. Ultrasound has been used efficiently for the synthesis of materials, degradation of organic compounds, and fuel production, among other applications. The recent increase in energy demands, as well as the stringent environmental stress due to pollution, have resulted in the need to develop green chemistry-based processes to generate and remove contaminants in a more environmentally friendly and cost-effective manner. It is possible to carry out the synthesis and deposition of catalysts inside the reactor using the ultrasound-promoted method in the microfluidic system. In addition, the synergistic effect generated by photocatalysis and sonochemistry in a microreactor can be used for the production of different chemicals, which have high value in the pharmaceutical and chemical industries. The current review highlights the use of both photocatalysis and sonochemistry for developing microreactors and their applications.

Electrochemical Reoxidation Enables Continuous Methane-to-Methanol Catalysis with Aqueous Pt Salts

The direct conversion of methane to methanol would enable better utilization of abundant natural gas resources. In the presence of stoichiometric Pt^IV oxidants, Pt^II ions are capable of catalyzing this reaction in aqueous solutions at modest temperatures. Practical implementation of this chemistry requires a viable strategy for replacing or regenerating the expensive Pt^IV oxidant. Herein, we establish an electrochemical strategy for continuous regeneration of the Pt^IV oxidant to furnish overall electrochemical methane oxidation. We show that Cl-adsorbed Pt electrodes catalyze facile oxidation of Pt^II to Pt^IV at low overpotential without concomitant methanol oxidation. Exploiting this facile electrochemistry, we maintain the Pt^II/IV ratio during Pt^II-catalyzed methane oxidation via in situ monitoring of the solution potential coupled with dynamic modulation of the electric current. This approach leads to sustained methane oxidation catalysis with 70% selectivity for methanol.

Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)2(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral α-β unsaturated γ-lactam. With two simultaneous objectives - high conversion and high diastereomeric excess - the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.

Designing Algorithms To Aid Discovery by Chemical Robots

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery.

Nitrate measurement in droplet flow: gas-mediated crosstalk and correction

In droplet microfluidics, droplets have traditionally been considered discrete self-contained reaction chambers, however recent work has shown that dissolved solutes can transfer into the oil phase and migrate into neighbouring droplets under certain conditions. The majority of reports on such inter-droplet "crosstalk" have focused on surfactant-driven mechanisms, such as transport within micelles. While trialling a droplet-based system for quantifying nitrate in water, we encountered crosstalk driven by a very different mechanism: conversion of the analyte to a gaseous intermediate which subsequently diffused between droplets. Importantly we found that the crosstalk occurred predictably, could be experimentally quantified, and measurements rationally post-corrected. This showed that droplet microfluidic systems susceptible to crosstalk such as this can nonetheless be used for quantitative analysis.

Click Chemistry Approaches to Expand the Repertoire of PEG-based Fluorinated Surfactants for Droplet Microfluidics

We report the novel and simplified synthesis of fluorinated surfactants for droplet microfluidics. The range of applications of droplet microfluidics has greatly expanded during the last decade thanks to its ability to manipulate and process tiny amount of sample and reagents at high throughput in independent reactors. A critical component of the technology is the formulation of the immiscible oil phase that contains surfactants to stabilize droplets. The success of droplet microfluidics relies mostly on a single fluorinated formulation that uses a PFPE–PEG triblock surfactant. The synthesis of this surfactant is laborious and requires skills in synthetic chemistry preventing the wider community to explore the synthesis of surfactants with alternate structures. We sought to provide a simplified synthesis for novel PFPE–PEG surfactants based on click chemistry approaches such as copper-catalyzed azide-alkyne cycloaddition (CuAAC) and UV-activated thiol–yne reactions. Our strategy is based on converting a moisture sensitive intermediate typically used in the synthesis of the triblock PFPE–PEG surfactant into a stable and click ready molecule. We successfully combined that fluorinated tail with differently functionalized PEG and glycerol ethoxylate molecules to generate surfactants with diverse structures via CuACC and thiol–yne reactions. We report the characterization, biocompatibility and ability to stabilize emulsions of those surfactants, as well as the unique advantages and challenges of the strategy.

Click-synthesis of fluorinated surfactants for droplet microfluidics.

Optimum catalyst selection over continuous and discrete process variables with a single droplet microfluidic reaction platform

A mixed-integer nonlinear program (MINLP) algorithm to optimize catalyst turnover number (TON) and product yield by simultaneously modulating discrete variables—catalyst types—and continuous variables—temperature, residence time, and catalyst loading—was implemented and validated.

Fluorescence-activated droplet sorting of lipolytic microorganisms using a compact optical system

Lipases are ubiquitous enzymes of great physiological significance that have been used extensively in multiple industries. Environmental microorganisms are a major source for the discovery of novel lipases with high catalytic efficiency and selectivity. However, current plate-based screening of lipase-producing strains is time consuming, labour intensive and inefficient. In this study, we developed an ultra-high throughput screening pipeline for lipase-producing strains based on fluorescence-activated droplet sorting (FADS) using a compact optical system that could be easily set up in an alignment-free manner. The pipeline includes droplet generation, droplet incubation, picoinjection of the fluorescence probe, and sorting of droplets with a throughput of 2 × 10⁶ drops per h. We applied the pipeline to screen samples collected from different locations, including sediments from a hot spring in Tibet, soils from the Zoige wetland, contaminated soils from an abandoned oilfield, and a Chinese Daqu starter. In total, we obtained 47 lipase-producing bacterial strains belonging to seven genera, including Staphylococcus, Bacillus, Enterobacter, Serratia, Prolinoborus, Acinetobacter, and Leclercia. We believe that this FADS-based pipeline could be extended to screen various enzymes from the environment, and may find wide applications in breeding of industrial microorganisms.

Why microfluidics? Merits and trends in chemical synthesis

The intrinsic limitations of conventional batch synthesis have hindered its applications in both solving classical problems and exploiting new frontiers. Microfluidic technology offers a new platform for chemical synthesis toward either molecules or materials, which has promoted the progress of diverse fields such as organic chemistry, materials science, and biomedicine. In this review, we focus on the improved performance of microreactors in handling various situations, and outline the trend of microfluidic synthesis (microsynthesis, μSyn) from simple microreactors to integrated microsystems. Examples of synthesizing both chemical compounds and micro/nanomaterials show the flexible applications of this approach. We aim to provide strategic guidance for the rational design, fabrication, and integration of microdevices for synthetic use. We critically evaluate the existing challenges and future opportunities associated with this burgeoning field.

Challenges and opportunities for alkane functionalisation using molecular catalysts

The conversion of vast low-value saturated hydrocarbons into valuable chemicals is of great interest.

The conversion of vast low-value saturated hydrocarbons into valuable chemicals is of great interest. Thanks to the progression of organometallic and coordination chemistry, transition metal catalysed C sp³–H bond functionalisation has now become a powerful tool for alkane transformations. Specifically, methods for alkane functionalisation include radical initiated C–H functionalisation, carbene/nitrene insertion, and transition metal catalysed C–H bond activation. This perspective provides a systematic and concise overview of each protocol, highlighting the factors that govern regioselectivity in these reactions. The challenges of the existing catalytic tactics and future directions for catalyst development in this field will be presented.

A droplet-chip/mass spectrometry approach to study organic synthesis at nanoliter scale

A droplet-based microfluidic device with seamless hyphenation to electrospray mass spectrometry was developed to rapidly investigate organic reactions in segmented flow providing a versatile tool for drug development. A chip-MS interface with an integrated counterelectrode allowed for a flexible positioning of the chip-emitter in front of the MS orifice as well as an independent adjustment of the electrospray potentials. This was necessary to avoid contamination of the mass spectrometer as well as sample overloading due to the high analyte concentrations. The device was exemplarily applied to study the scope of an amino-catalyzed domino reaction with low picomole amount of catalyst in individual nanoliter sized droplets.

Emerging Droplet Microfluidics

Droplet microfluidics generates and manipulates discrete droplets through immiscible multiphase flows inside microchannels. Due to its remarkable advantages, droplet microfluidics bears significant value in an extremely wide range of area. In this review, we provide a comprehensive and in-depth insight into droplet microfluidics, covering fundamental research from microfluidic chip fabrication and droplet generation to the applications of droplets in bio(chemical) analysis and materials generation. The purpose of this review is to convey the fundamentals of droplet microfluidics, a critical analysis on its current status and challenges, and opinions on its future development. We believe this review will promote communications among biology, chemistry, physics, and materials science.

Computational Modeling and Analysis of Fluid Structure Interaction in Micromixers with Deformable Baffle

A passive micromixer with obstacles in the form of deformable baffles is examined numerically. The model deploys an Arbitrary Lagrangian-Eulerian framework with Fluid-structure interaction coupled with a diffusion–advection model. Numerical analysis is carried out in the Reynolds number [Re] range of 0.01≤Re≤300. The objective of the present study is to enhance mixing between two component flow streams in a microchannel encompassing a deformable baffle. In the present work, the baffle deforms only due to the dynamic force of fluids. No external forces are applied. To exemplify the effectiveness of the present design, water and a suspension of curcumin drug loaded nanoparticles are taken as two fluids. Mixing index based on the variance of the local concentration of the suspension is employed to appraise the mixing performance of the micromixer. The introduction of the deformable baffle in a micromixer proliferates the mixing performance with minimal pressure drop over the tested Reynolds number range.

On-chip integration of organic synthesis and HPLC/MS analysis for monitoring stereoselective transformations at the micro-scale

We present a microfluidic system, seamlessly integrating microflow and microbatch synthesis with a HPLC/nano-ESI-MS functionality on a single glass chip. The microfluidic approach allows to efficiently steer and dispense sample streams down to the nanoliter-range for studying reactions in quasi real-time. In a proof-of-concept study, the system was applied to explore amino-catalyzed reactions, including asymmetric iminium-catalyzed Friedel-Crafts alkylations in microflow and micro confined reaction vessels.

Suzuki-Miyaura cross-coupling optimization enabled by automated feedback

An automated, droplet-flow microfluidic system explores and optimizes Pd-catalyzed Suzuki-Miyaura cross-coupling reactions. A smart optimal DoE-based algorithm is implemented to increase the turnover number and yield of the catalytic system considering both discrete variables-palladacycle and ligand-and continuous variables-temperature, time, and loading-simultaneously. The use of feedback allows for experiments to be run with catalysts and under conditions more likely to produce an optimum; consequently complex reaction optimizations are completed within 96 experiments. Response surfaces predicting reaction performance near the optima are generated and validated. From the screening results, shared attributes of successful precatalysts are identified, leading to improved understanding of the influence of ligand selection upon transmetalation and oxidative addition in the reaction mechanism. Dialkylbiarylphosphine, trialkylphosphine, and bidentate ligands are assessed.

Combinatorial microfluidic droplet engineering for biomimetic material synthesis

Although droplet-based systems are used in a wide range of technologies, opportunities for systematically customizing their interface chemistries remain relatively unexplored. This article describes a new microfluidic strategy for rapidly tailoring emulsion droplet compositions and properties. The approach uses a simple platform for screening arrays of droplet-based microfluidic devices and couples this with combinatorial selection of the droplet compositions. Through the application of genetic algorithms over multiple screening rounds, droplets with target properties can be rapidly generated. The potential of this method is demonstrated by creating droplets with enhanced stability, where this is achieved by selecting carrier fluid chemistries that promote titanium dioxide formation at the droplet interfaces. The interface is a mixture of amorphous and crystalline phases, and the resulting composite droplets are biocompatible, supporting in vitro protein expression in their interiors. This general strategy will find widespread application in advancing emulsion properties for use in chemistry, biology, materials, and medicine.

Synergy of Microfluidics and Ultrasound : Process Intensification Challenges and Opportunities

A compact snapshot of the current convergence of novel developments relevant to chemical engineering is given. Process intensification concepts are analysed through the lens of microfluidics and sonochemistry. Economical drivers and their influence on scientific activities are mentioned, including innovation opportunities towards deployment into society. We focus on the control of cavitation as a means to improve the energy efficiency of sonochemical reactors, as well as in the solids handling with ultrasound; both are considered the most difficult hurdles for its adoption in a practical and industrial sense. Particular examples for microfluidic clogging prevention, numbering-up and scaling-up strategies are given. To conclude, an outlook of possible new directions of this active and promising combination of technologies is hinted.

Feedback in Flow for Accelerated Reaction Development

The pharmaceutical industry is investing in continuous flow and high-throughput experimentation as tools for rapid process development accelerated scale-up. Coupled with automation, these technologies offer the potential for comprehensive reaction characterization and optimization, but with the cost of conducting exhaustive multifactor screens. Automated feedback in flow offers researchers an alternative strategy for efficient characterization of reactions based on the use of continuous technology to control chemical reaction conditions and optimize in lieu of screening. Optimization with feedback allows experiments to be conducted where the most information can be gained from the chemistry, enabling product yields to be maximized and kinetic models to be generated while the total number of experiments is minimized. This Account opens by reviewing select examples of feedback optimization in flow and applications to chemical research. Systems in the literature are classified into (i) deterministic "black box" optimization systems that do not model the reaction system and are therefore limited in the utility of results for scale-up, (ii) deterministic model-based optimization systems from which reaction kinetics and/or mechanisms can be automatically evaluated, and (iii) stochastic systems. Though diverse in application, flow feedback systems have predominantly focused upon the optimization of continuous variables, i.e., variables such as time, temperature, and concentration that can be ramped from one experiment to the next. Unfortunately, this implies that the screening of discrete variables such as catalyst, ligand, or solvent generally does not factor into automated flow optimization, resulting in incomplete process knowledge. Herein, we present a system and strategy developed for optimizing discrete and continuous variables of a chemical reaction simultaneously. The approach couples automated feedback with high-throughput reaction screening in droplet flow microfluidics. This Account details the system configuration for on-demand creation of sub-20 μL droplets with interchangeable reagents and catalysts. These droplets are reacted in a fully automated microfluidic system and analyzed online by LC/MS. Feeding back from the online analytical results, a design of experiments (DoE)-based adaptive response surface algorithm is employed that deductively removes candidate reagents from the optimization as optimal reaction conditions are refined, leading to rapid convergence. Using the automated optimization platform, case studies are presented for solvent selection in a competitive alkylation chemistry and for catalyst-ligand selection in heteroaromatic Suzuki-Miyaura cross-coupling chemistries. For the monoalkylation of trans-1,2-diaminocyclohexane, polar aprotic solvents at moderate temperatures are shown to be favorable, with optimality accurately identified with dimethyl sulfoxide as the solvent in 67 experiments. For Suzuki-Miyaura cross-couplings, the optimality of precatalysts and continuous variable conditions are observed to change in accordance with the coupling reagents, providing insights into catalyst behavior in the context of the reaction mechanism. Future opportunities in automated reaction development include the incorporation of chemoinformatics for faster analysis and machine-learning algorithms to guide and optimize the synthesis. Adoption of this technology stands to reduce graduate student and postdoc time on routine tasks in the laboratory, while feeding back knowledge used to guide new research directions. Moreover, the application of this technology in industry promises to lessen the cost and time associated with advancing pharmaceutical molecules through development and scale-up.

Discovery and Optimization of Materials Using Evolutionary Approaches

Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries.

Microprocessor-based integration of microfluidic control for the implementation of automated sensor monitoring and multithreaded optimization algorithms

Microfluidic applications range from combinatorial synthesis to high throughput screening, with platforms integrating analog perfusion components, digitally controlled micro-valves and a range of sensors that demand a variety of communication protocols. Currently, discrete control units are used to regulate and monitor each component, resulting in scattered control interfaces that limit data integration and synchronization. Here, we present a microprocessor-based control unit, utilizing the MS Gadgeteer open framework that integrates all aspects of microfluidics through a high-current electronic circuit that supports and synchronizes digital and analog signals for perfusion components, pressure elements, and arbitrary sensor communication protocols using a plug-and-play interface. The control unit supports an integrated touch screen and TCP/IP interface that provides local and remote control of flow and data acquisition. To establish the ability of our control unit to integrate and synchronize complex microfluidic circuits we developed an equi-pressure combinatorial mixer. We demonstrate the generation of complex perfusion sequences, allowing the automated sampling, washing, and calibrating of an electrochemical lactate sensor continuously monitoring hepatocyte viability following exposure to the pesticide rotenone. Importantly, integration of an optical sensor allowed us to implement automated optimization protocols that require different computational challenges including: prioritized data structures in a genetic algorithm, distributed computational efforts in multiple-hill climbing searches and real-time realization of probabilistic models in simulated annealing. Our system offers a comprehensive solution for establishing optimization protocols and perfusion sequences in complex microfluidic circuits.