Artificial intelligence and automation in computer aided synthesis planning DOI
Amol Thakkar, Simon Johansson, Kjell Jorner

и другие.

Reaction Chemistry & Engineering, Год журнала: 2020, Номер 6(1), С. 27 - 51

Опубликована: Ноя. 5, 2020

In this perspective we deal with questions pertaining to the development of synthesis planning technologies over course recent years.

Язык: Английский

Technological Innovations in Photochemistry for Organic Synthesis: Flow Chemistry, High-Throughput Experimentation, Scale-up, and Photoelectrochemistry DOI
Laura Buglioni, Fabian Raymenants, Aidan Slattery

и другие.

Chemical Reviews, Год журнала: 2021, Номер 122(2), С. 2752 - 2906

Опубликована: Авг. 10, 2021

Photoinduced chemical transformations have received in recent years a tremendous amount of attention, providing plethora opportunities to synthetic organic chemists. However, performing photochemical transformation can be quite challenge because various issues related the delivery photons. These challenges barred widespread adoption steps industry. past decade, several technological innovations led more reproducible, selective, and scalable photoinduced reactions. Herein, we provide comprehensive overview these exciting advances, including flow chemistry, high-throughput experimentation, reactor design scale-up, combination photo- electro-chemistry.

Язык: Английский

Процитировано

544

On scientific understanding with artificial intelligence DOI Open Access
Mario Krenn, Robert Pollice, Si Yue Guo

и другие.

Nature Reviews Physics, Год журнала: 2022, Номер 4(12), С. 761 - 769

Опубликована: Окт. 11, 2022

Язык: Английский

Процитировано

226

A Brief Introduction to Chemical Reaction Optimization DOI Creative Commons
Connor J. Taylor, Alexander Pomberger, Kobi Felton

и другие.

Chemical Reviews, Год журнала: 2023, Номер 123(6), С. 3089 - 3126

Опубликована: Фев. 23, 2023

From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing scientist to develop practical skills some chemical intuition. This procedure is often kept long into researcher's career, as new developed similar protocols, intuition-guided deviations through learning failed experiments. However, when attempting understand systems interest, it has been shown model-based, algorithm-based, miniaturized high-throughput techniques outperform human intuition optimization in much more time- material-efficient manner; this covered detail paper. As many chemists not exposed these undergraduate teaching, leads disproportionate number scientists wish optimize their reactions but unable use methodologies or simply unaware existence. review highlights basics, cutting-edge, modern well its relation process scale-up can thereby serve reference for inspired each techniques, detailing several respective applications.

Язык: Английский

Процитировано

219

A field guide to flow chemistry for synthetic organic chemists DOI Creative Commons
Luca Capaldo, Zhenghui Wen, Timothy Noël

и другие.

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

Процитировано

196

How to approach flow chemistry DOI Creative Commons
Mara Guidi, Peter H. Seeberger,

Kerry Gilmore

и другие.

Chemical Society Reviews, Год журнала: 2020, Номер 49(24), С. 8910 - 8932

Опубликована: Янв. 1, 2020

The intrinsic attributes of flow chemistry that facilitate and provide reproducible access to a range processes are best exploited using modules targeting an overall effect: selective transformation or the generation reactive intermediate.

Язык: Английский

Процитировано

191

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab DOI Creative Commons
Martin Seifrid, Robert Pollice, Andrés Aguilar-Gránda

и другие.

Accounts of Chemical Research, Год журнала: 2022, Номер 55(17), С. 2454 - 2466

Опубликована: Авг. 10, 2022

We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter of discovery requires faster research development cycles enabled by better integration between hypothesis generation, design, experimentation, data analysis. Typical take months years. However, data-driven automated laboratories, or self-driving can significantly molecular materials discovery. Recently, substantial have been made in areas machine learning optimization algorithms that allowed researchers extract valuable knowledge from multidimensional sets. Machine models be trained on large sets literature databases, but their performance often hampered a lack negative results metadata. In contrast, generated laboratories information-rich, containing precise details experimental conditions Consequently, much larger amounts high-quality are gathered laboratories. When placed open repositories, this used community reproduce experiments, for more in-depth analysis, as basis further investigation. Accordingly, will increase accessibility reproducibility science, is sorely needed.In Account, describe our efforts build lab new class materials: organic semiconductor lasers (OSLs). Since they only recently demonstrated, little known about material design rules thin-film, electrically-pumped OSL devices compared other technologies such light-emitting diodes photovoltaics. To realize high-performing materials, developing flexible system synthesis via iterative Suzuki-Miyaura cross-coupling reactions. platform directly coupled analysis purification capabilities. Subsequently, molecules interest transferred an optical characterization setup. currently limited measurements solution. properties ultimately most important solid state (e.g., thin-film device). end different scientific goal, inorganic focused oxygen evolution reaction.While future very promising, numerous challenges still need overcome. These split into cognition motor function. Generally, cognitive related with constraints unexpected outcomes general algorithmic solutions yet developed. A practical challenge could resolved near software control because few instrument manufacturers products mind. Challenges function largely handling heterogeneous systems, dispensing solids performing extractions. As result, it critical understand adapting procedures were designed human experimenters not simple transferring those same actions system, there may efficient ways achieve goal fashion. carefully rethink translation manual protocols.

Язык: Английский

Процитировано

150

Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling DOI
Nicholas H. Angello, Vandana Rathore, Wiktor Beker

и другие.

Science, Год журнала: 2022, Номер 378(6618), С. 399 - 405

Опубликована: Окт. 27, 2022

General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction requires considering vast space derived from a large matrix substrates crossed with high-dimensional conditions, rendering exhaustive experimentation impractical. Here, we report simple closed-loop workflow that leverages data-guided down-selection, uncertainty-minimizing machine learning, robotic discover conditions. Application the challenging consequential problem heteroaryl Suzuki-Miyaura cross-coupling identified double average yield relative widely used benchmark was previously developed using traditional approaches. This study provides practical road map solving multidimensional optimization problems search spaces.

Язык: Английский

Процитировано

132

Advanced Real‐Time Process Analytics for Multistep Synthesis in Continuous Flow** DOI Creative Commons
Peter Sagmeister,

René Lebl,

Ismaël Castillo

и другие.

Angewandte Chemie International Edition, Год журнала: 2021, Номер 60(15), С. 8139 - 8148

Опубликована: Янв. 15, 2021

In multistep continuous flow chemistry, studying complex reaction mixtures in real time is a significant challenge, but provides an opportunity to enhance understanding and control. We report the integration of four complementary process analytical technology tools (NMR, UV/Vis, IR UHPLC) synthesis active pharmaceutical ingredient, mesalazine. This synthetic route exploits processing for nitration, high temperature hydrolysis hydrogenation reactions, as well three inline separations. Advanced data analysis models were developed (indirect hard modeling, deep learning partial least squares regression), quantify desired products, intermediates impurities time, at multiple points along pathway. The capabilities system have been demonstrated by operating both steady state dynamic experiments represents step forward data-driven synthesis.

Язык: Английский

Процитировано

129

Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform DOI Creative Commons
Anirudh M. K. Nambiar, C. Breen, Travis Hart

и другие.

ACS Central Science, Год журнала: 2022, Номер 8(6), С. 825 - 836

Опубликована: Июнь 10, 2022

Computer-aided synthesis planning (CASP) tools can propose retrosynthetic pathways and forward reaction conditions for the of organic compounds, but limited availability context-specific data currently necessitates experimental development to fully specify process details. We plan optimize a CASP-proposed human-refined multistep route toward an exemplary small molecule, sonidegib, on modular, robotic flow platform with integrated analytical technology (PAT) data-rich experimentation. Human insights address catalyst deactivation improve yield by strategic choices order addition. Multi-objective Bayesian optimization identifies optimal values categorical continuous variables in involving 3 reactions (including heterogeneous hydrogenation) 1 separation. The platform's modularity, reconfigurability, flexibility convergent are shown be essential allowing variation downstream residence time processes controlling addition minimize undesired reactivity. Overall, work demonstrates how automation, machine learning, robotics enhance manual experimentation through assistance idea generation, design, execution, optimization.

Язык: Английский

Процитировано

122

Toward Machine Learning-Enhanced High-Throughput Experimentation DOI Creative Commons
Natalie S. Eyke, Brent A. Koscher, Klavs F. Jensen

и другие.

Trends in Chemistry, Год журнала: 2021, Номер 3(2), С. 120 - 132

Опубликована: Янв. 2, 2021

Язык: Английский

Процитировано

114