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.

Self-driving laboratories: A paradigm shift in nanomedicine development

Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial, in part due to the complexity associated with their preclinical development. Herein, we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next-generation nanomedicines but also encourage participation of the pharmaceutical science community in a large data curation initiative.

Quid Pro Flow

How do you get into flow? We trained in flow chemistry during postdoctoral research and are now applying it in new areas: materials chemistry, crystallization, and supramolecular synthesis. Typically, when researchers think of "flow", they are considering predominantly liquid-based organic synthesis; application to other disciplines comes with its own challenges. In this Perspective, we highlight why we use and champion flow technologies in our fields, summarize some of the questions we encounter when discussing entry into flow research, and suggest steps to make the transition into the field, emphasizing that communication and collaboration between disciplines is key.

Simultaneous self-optimisation of yield and purity through successive combination of inline FT-IR spectroscopy and online mass spectrometry in flow reactions

Self-optimisation constitutes a very helpful tool for chemical process development, both in lab and in industrial applications. However, research on the application of model-free autonomous optimisation strategies (based on experimental investigation) for complex reactions of high industrial significance, which involve considerable intermediate and by-product formation, is still in an early stage. This article describes the development of an enhanced autonomous microfluidic reactor platform for organolithium and epoxide reactions that incorporates a successive combination of inline FT-IR spectrometer and online mass spectrometer. Experimental data is collected in real-time and used as feedback for the optimisation algorithms (modified Simplex algorithm and Design of Experiments) without time delay. An efficient approach to handle intricate optimisation problems is presented, where the inline FT-IR measurements are used to monitor the reaction’s main components, whereas the mass spectrometer’s high sensitivity permits insights into the formation of by-products. To demonstrate the platform’s flexibility, optimal reaction conditions of two organic syntheses are identified. Both pose several challenges, as complex reaction mechanisms are involved, leading to a large number of variable parameters, and a considerable amount of by-products is generated under non-ideal process conditions. Through multidimensional real-time optimisation, the platform supersedes labor- and cost-intensive work-up procedures, while diminishing waste generation, too. Thus, it renders production processes more efficient and contributes to their overall sustainability.

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Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions

Innovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipment used in the laboratory. Artificial intelligence (AI) technologies are the next step in developing the solution for practical problems in science, including the development of new materials. Those technologies go broadly beyond the borders of a computer science branch and give new insights and practical possibilities within the far areas of expertise and chemistry applications. One of the attractive challenges is an automated new functional material synthesis driven by AI. However, while having many years of hands-on experience, chemistry specialists have a vague picture of AI. To strengthen and underline AI's role in materials discovery, a short introduction is given to the essential technologies, and the machine learning process is explained. After this review, this review summarizes the recent studies of new strategies that help automate and accelerate the development of new functional materials. Moreover, automatized laboratories' self-driving cycle could benefit from using AI algorithms to optimize new functional nanomaterials' synthetic routes. Despite the fact that such technologies will shape material science in the nearest future, we note the intelligent use of algorithms and automation is required for novel discoveries.

A tutored discourse on microcontrollers, single board computers and their applications to monitor and control chemical reactions

This Tutored Discourse constitutes a preliminary exposure on how synthesis chemists can engage positively with inexpensive, low-power microcontrollers to aid control, monitoring and optimisation of chemical reactions.

Self-optimising processes and real-time-optimisation of organic syntheses in a microreactor system using Nelder–Mead and design of experiments

Comparing an enhanced simplex algorithm with model-free design of experiments, this work presents a flexible platform for multi-objective, real-time optimisation.

Reconfigurable Flow Platform for Automated Reagent Screening and Autonomous Optimization for Bioinspired Lignans Synthesis

Naturally occurring benzoxanthenones, which belong to the vast family of lignans, are promising biologically relevant targets. They are biosynthetically produced by the oxidative dimerization of 2-propenyl phenols. In this manuscript, we disclose a powerful automated flow-based strategy for identifying and optimizing a cobalt-catalyzed oxidizing system for the bioinspired dimerization of 2-propenyl phenols. We designed a reconfigurable flow reactor associating online monitoring and process control instrumentation. Our machine was first configured as an automated screening platform to evaluate a matrix of 4 catalysts (plus the blank) and 5 oxidants (plus the blank) at two different temperatures, resulting in an array of 50 reactions. The automated screening was conducted on micromole scale at a rate of one fully characterized reaction every 26 min. After having identified the most promising cobalt-catalyzed oxidizing system, the automated screening platform was straightforwardly reconfigured to an autonomous self-optimizing flow reactor by implementation of an optimization algorithm in the closed-loop system. The optimization campaign allowed the determination of very effective experimental conditions in a limited number of experiments, which allowed us to prepare the natural products carpanone and polemannone B as well as synthetic analogues.

Integrating continuous flow synthesis with in-line analysis and data generation

Continuous flow synthesis of fine chemicals has successfully advanced from an academic niche area to a rapidly growing field of its own that directly impacts developments and applications in industrial settings. Whilst the numerous advantages of flow over batch processing are widely recognised and have led to a wider uptake of continuous flow synthesis within the community, we have reached a point where continuous flow synthesis has to transition from a stand-alone enabling technology to a readily integrated synthesis concept. Thus it is paramount to embrace a multitude of in-line analysis and purification techniques to not only allow for efficiently telescoped multi-step sequences but ultimately generate bioactivity data concomitantly on newly synthesised entities. This short review summarises the state of the art in this field and presents both challenges and opportunities that arise from this ambitious endeavour.

An autonomous microreactor platform for the rapid identification of kinetic models

Rapid estimation of kinetic parameters with high precision is facilitated by automation combined with online Model-Based Design of Experiments.

Adaptive and automated system-optimization for heterogeneous flow-hydrogenation reactions

The autonomous hydrogenation of carbonyl compounds and N-heterocycles over solid catalysts with H₂ is achieved in 3D parameter space by integrating analytics, control and hardware.

Across-the-World Automated Optimization and Continuous-Flow Synthesis of Pharmaceutical Agents Operating Through a Cloud-Based Server

The power of the Cloud has been harnessed for pharmaceutical compound production with remote servers based in Tokyo, Japan being left to autonomously find optimal synthesis conditions for three active pharmaceutical ingredients (APIs) in laboratories in Cambridge, UK. A researcher located in Los Angeles, USA controlled the entire process via an internet connection. The constituent synthetic steps for Tramadol, Lidocaine, and Bupropion were thus optimized with minimal intervention from operators within hours, yielding conditions satisfying customizable evaluation functions for all examples.

The Generation of Diazo Compounds in Continuous-Flow

Toxic, cancerogenic and explosive-these attributes are typically associated with diazo compounds. Nonetheless, diazo compounds are nowadays a highly demanded class of reagents for organic synthesis, yet the concerns with regards to safe and scalable transformations of these compounds are still exceptionally high. Lately, the research area of the continuous-flow synthesis of diazo compounds attracted significant interest and a whole variety of protocols for their "on-demand" preparation have been realized to date. This concept article focuses on the recent developments using continuous-flow technologies to access diazo compounds; thus minimizing risks and hazards when working with this particular class of compounds. In this article we discuss these concepts and highlight different pre-requisites to access and to perform downstream functionalization reaction.

Flow reactors integrated with in-line monitoring using benchtop NMR spectroscopy

The state-of-the-art flow reactors integrated with in-line benchtop NMR are thoroughly discussed with highlights on the strengths and weaknesses of this emerging technology.

A machine-assisted approach for the preparation of follow-on pharmaceutical compound libraries

Coupling of a continuous flow process to a robotic microwave device has led to a simple platform for the preparation of compound libraries, specifically the anti-migraine compound Zolmitriptan and several unknown analogues.

Optimizing Chemical Reactions with Deep Reinforcement Learning

Deep reinforcement learning was employed to optimize chemical reactions. Our model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome. This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions. Furthermore, we introduced an efficient exploration strategy by drawing the reaction conditions from certain probability distributions, which resulted in an improvement on regret from 0.062 to 0.039 compared with a deterministic policy. Combining the efficient exploration policy with accelerated microdroplet reactions, optimal reaction conditions were determined in 30 min for the four reactions considered, and a better understanding of the factors that control microdroplet reactions was reached. Moreover, our model showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability.

The Hitchhiker's Guide to Flow Chemistry ∥

Flow chemistry involves the use of channels or tubing to conduct a reaction in a continuous stream rather than in a flask. Flow equipment provides chemists with unique control over reaction parameters enhancing reactivity or in some cases enabling new reactions. This relatively young technology has received a remarkable amount of attention in the past decade with many reports on what can be done in flow. Until recently, however, the question, "Should we do this in flow?" has merely been an afterthought. This review introduces readers to the basic principles and fundamentals of flow chemistry and critically discusses recent flow chemistry accounts.

Continuous N-alkylation reactions of amino alcohols using γ-Al2O3 and supercritical CO2: unexpected formation of cyclic ureas and urethanes by reaction with CO2

The use of γ-Al₂O₃ as a heterogeneous catalyst in scCO₂ has been successfully applied to the amination of alcohols for the synthesis of N-alkylated heterocycles. The optimal reaction conditions (temperature and substrate flow rate) were determined using an automated self-optimising reactor, resulting in moderate to high yields of the target products. Carrying out the reaction in scCO₂ was shown to be beneficial, as higher yields were obtained in the presence of CO₂ than in its absence. A surprising discovery is that, in addition to cyclic amines, cyclic ureas and urethanes could be synthesised by incorporation of CO₂ from the supercritical solvent into the product.

Continuous-flow synthesis of fluorine-containing fine chemicals with integrated benchtop NMR analysis

Fluorine fine chemical synthesis takes a step ahead using integrated NMR online-analysis with a benchtop NMR spectrometer.

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.

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.

Enabling Technologies for the Future of Chemical Synthesis

Technology is evolving at breakneck pace, changing the way we communicate, travel, find out information, and live our lives. Yet chemistry as a science has been slower to adapt to this rapidly shifting world. In this Outlook we use highlights from recent literature reports to describe how progresses in enabling technologies are altering this trend, permitting chemists to incorporate new advances into their work at all levels of the chemistry development cycle. We discuss the benefits and challenges that have arisen, impacts on academic-industry relationships, and future trends in the area of chemical synthesis.

Applications of Continuous-Flow Photochemistry in Organic Synthesis, Material Science, and Water Treatment

Continuous-flow photochemistry in microreactors receives a lot of attention from researchers in academia and industry as this technology provides reduced reaction times, higher selectivities, straightforward scalability, and the possibility to safely use hazardous intermediates and gaseous reactants. In this review, an up-to-date overview is given of photochemical transformations in continuous-flow reactors, including applications in organic synthesis, material science, and water treatment. In addition, the advantages of continuous-flow photochemistry are pointed out and a thorough comparison with batch processing is presented.

A benchtop NMR spectrometer as a tool for monitoring mesoscale continuous-flow organic synthesis: equipment interface and assessment in four organic transformations

An approach is reported for monitoring continuous-flow reactions by means of a low-field benchtop NMR spectrometer.

Online monitoring and analysis for autonomous continuous flow self-optimizing reactor systems

In this review the recent progress in the field of self-optimizing reactor systems for continuous flow chemistry is presented.

Unlocking the potential of supported liquid phase catalysts with supercritical fluids: low temperature continuous flow catalysis with integrated product separation

Solution-phase catalysis using molecular transition metal complexes is an extremely powerful tool for chemical synthesis and a key technology for sustainable manufacturing. However, as the reaction complexity and thermal sensitivity of the catalytic system increase, engineering challenges associated with product separation and catalyst recovery can override the value of the product. This persistent downstream issue often renders industrial exploitation of homogeneous catalysis uneconomical despite impressive batch performance of the catalyst. In this regard, continuous-flow systems that allow steady-state homogeneous turnover in a stationary liquid phase while at the same time effecting integrated product separation at mild process temperatures represent a particularly attractive scenario. While continuous-flow processing is a standard procedure for large volume manufacturing, capitalizing on its potential in the realm of the molecular complexity of organic synthesis is still an emerging area that requires innovative solutions. Here we highlight some recent developments which have succeeded in realizing such systems by the combination of near- and supercritical fluids with homogeneous catalysts in supported liquid phases. The cases discussed exemplify how all three levels of continuous-flow homogeneous catalysis (catalyst system, separation strategy, process scheme) must be matched to locate viable process conditions.

Organic synthesis: march of the machines

Organic synthesis is changing; in a world where budgets are constrained and the environmental impacts of practice are scrutinized, it is increasingly recognized that the efficient use of human resource is just as important as material use. New technologies and machines have found use as methods for transforming the way we work, addressing these issues encountered in research laboratories by enabling chemists to adopt a more holistic systems approach in their work. Modern developments in this area promote a multi-disciplinary approach and work is more efficient as a result. This Review focuses on the concepts, procedures and methods that have far-reaching implications in the chemistry world. Technologies have been grouped as topics of opportunity and their recent applications in innovative research laboratories are described.