The rise of self-driving labs in chemical and materials sciences

Nature Synthesis, Год журнала: 2023, Номер 2(6), С. 483 - 492

Опубликована: Янв. 30, 2023

Язык: Английский

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A field guide to flow chemistry for synthetic organic chemists

Chemical Science, Год журнала: 2023, Номер 14(16), С. 4230 - 4247

Опубликована: Янв. 1, 2023

This review explores the benefits of flow chemistry and dispels notion that it is a mysterious “black box”, demonstrating how can push boundaries organic synthesis through understanding its governing principles.

Язык: Английский

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A Multi-Objective Active Learning Platform and Web App for Reaction Optimization

Journal of the American Chemical Society, Год журнала: 2022, Номер 144(43), С. 19999 - 20007

Опубликована: Окт. 19, 2022

We report the development of an open-source experimental design via Bayesian optimization platform for multi-objective reaction optimization. Using high-throughput experimentation (HTE) and virtual screening data sets containing high-dimensional continuous discrete variables, we optimized performance by fine-tuning algorithm components such as encodings, surrogate model parameters, initialization techniques. Having established framework, applied optimizer to real-world test scenarios simultaneous yield enantioselectivity in a Ni/photoredox-catalyzed enantioselective cross-electrophile coupling styrene oxide with two different aryl iodide substrates. Starting no previous data, identified conditions that surpassed previously human-driven campaigns within 15 24 experiments, each substrate, among 1728 possible configurations available To make more accessible nonexperts, developed graphical user interface (GUI) can be accessed online through web-based application incorporated features condition modification on fly visualization. This web does not require software installation, removing any programming barrier use platform, which enables chemists integrate routines into their everyday laboratory practices.

Язык: Английский

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Automated self-optimization, intensification, and scale-up of photocatalysis in flow

Science, Год журнала: 2024, Номер 383(6681)

Опубликована: Янв. 25, 2024

The optimization, intensification, and scale-up of photochemical processes constitute a particular challenge in manufacturing environment geared primarily toward thermal chemistry. In this work, we present versatile flow-based robotic platform to address these challenges through the integration readily available hardware custom software. Our open-source combines liquid handler, syringe pumps, tunable continuous-flow photoreactor, inexpensive Internet Things devices, an in-line benchtop nuclear magnetic resonance spectrometer enable automated, data-rich optimization with closed-loop Bayesian strategy. A user-friendly graphical interface allows chemists without programming or machine learning expertise easily monitor, analyze, improve photocatalytic reactions respect both continuous discrete variables. system's effectiveness was demonstrated by increasing overall reaction yields improving space-time compared those previously reported processes.

Язык: Английский

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AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Март 14, 2023

Closed-loop, autonomous experimentation enables accelerated and material-efficient exploration of large reaction spaces without the need for user intervention. However, advanced materials with complex, multi-step processes data sparse environments remains a challenge. In this work, we present AlphaFlow, self-driven fluidic lab capable discovery complex chemistries. AlphaFlow uses reinforcement learning integrated modular microdroplet reactor performing steps variable sequence, phase separation, washing, continuous in-situ spectral monitoring. To demonstrate power toward high dimensionality chemistries, use to discover optimize synthetic routes shell-growth core-shell semiconductor nanoparticles, inspired by colloidal atomic layer deposition (cALD). Without prior knowledge conventional cALD parameters, successfully identified optimized novel route, up 40 that outperformed sequences. Through capabilities closed-loop, learning-guided systems in exploring solving challenges nanoparticle syntheses, while relying solely on in-house generated from miniaturized microfluidic platform. Further application chemistries beyond can lead fundamental generation as well route discoveries optimization.

Язык: Английский

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Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

Journal of the American Chemical Society, Год журнала: 2022, Номер 145(1), С. 110 - 121

Опубликована: Дек. 27, 2022

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe machine learning workflow for multi-objective optimization catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through two sequential required in asymmetric synthesis an active pharmaceutical ingredient. To accomplish this, density functional theory-derived database >550 ligands constructed, designer chemical space mapping technique established. The protocol used classification methods identify catalysts, followed by linear regression model selectivity. led prediction validation significantly improved all outputs, suggesting general strategy can be readily implemented optimizations where performance is controlled

Язык: Английский

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Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back

Science, Год журнала: 2023, Номер 382(6677)

Опубликована: Дек. 21, 2023

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In first study, experimentally realized 294 unreported across three automatic iterations design-make-test-analyze cycles while exploring structure-function space four rarely reported scaffolds. each iteration, property prediction models that guided exploration learned structure-property diverse scaffold derivatives, which were multistep syntheses a variety reactions. The second study exploited trained explored chemical previously discover nine top-performing within lightly space.

Язык: Английский

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Self-Driving Laboratories for Chemistry and Materials Science

Chemical Reviews, Год журнала: 2024, Номер 124(16), С. 9633 - 9732

Опубликована: Авг. 13, 2024

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

Язык: Английский

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Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory

Nature Chemical Engineering, Год журнала: 2024, Номер 1(3), С. 240 - 250

Опубликована: Фев. 27, 2024

Ligands play a crucial role in enabling challenging chemical transformations with transition metal-mediated homogeneous catalysts. Despite their undisputed catalysis, discovery and development of ligands have proven to be resource-intensive undertaking. Here, response, we present self-driving catalysis laboratory, Fast-Cat, for autonomous resource-efficient parameter space navigation Pareto-front mapping high-temperature, high-pressure, gas–liquid reactions. Fast-Cat enables ligand benchmarking multi-objective catalyst performance evaluation minimal human intervention. Specifically, utilize perform rapid identification the hydroformylation reaction between syngas (CO H2) olefin (1-octene) presence rhodium various classes phosphorus-based ligands. By reactor benchmarking, demonstrate Fast-Cat's knowledge scalability, essential fine/specialty industries. We report details modular flow chemistry platform its experiment-selection strategy generation optimized experimental conditions in-house data required supplying machine-learning approaches investigations. A is presented efficient high-throughput screening using rhodium-catalyzed as case study. used Pareto map investigate varying several

Язык: Английский

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Bayesian Self‐Optimization for Telescoped Continuous Flow Synthesis

Angewandte Chemie International Edition, Год журнала: 2022, Номер 62(3)

Опубликована: Ноя. 8, 2022

The optimization of multistep chemical syntheses is critical for the rapid development new pharmaceuticals. However, concatenating individually optimized reactions can lead to inefficient syntheses, owing interdependencies between steps. Herein, we develop an automated continuous flow platform simultaneous telescoped reactions. Our approach applied a Heck cyclization-deprotection reaction sequence, used in synthesis precursor 1-methyltetrahydroisoquinoline C5 functionalization. A simple method multipoint sampling with single online HPLC instrument was designed, enabling accurate quantification each reaction, and in-depth understanding pathways. Notably, integration Bayesian techniques identified 81 % overall yield just 14 h, revealed favorable competing pathway formation desired product.

Язык: Английский

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