Machine Learning Force Fields

Chemical Reviews, Год журнала: 2021, Номер 121(16), С. 10142 - 10186

Опубликована: Март 11, 2021

In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out reach due to complexity traditional electronic-structure methods. One most promising applications is construction ML-based force fields (FFs), with aim narrow gap between accuracy ab initio methods and efficiency classical FFs. The key idea learn statistical relation chemical structure potential energy without relying on a preconceived notion fixed bonds or knowledge about relevant interactions. Such universal ML approximations are principle only limited by quality quantity reference data used train them. This review gives an overview ML-FFs insights that can be obtained from core concepts underlying described detail, step-by-step guide for constructing testing them scratch given. text concludes discussion challenges remain overcome next generation ML-FFs.

Язык: Английский

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C–H Activation: Toward Sustainability and Applications

ACS Central Science, Год журнала: 2021, Номер 7(2), С. 245 - 261

Опубликована: Фев. 2, 2021

Since the definition of "12 Principles Green Chemistry" more than 20 years ago, chemists have become increasingly mindful need to conserve natural resources and protect environment through judicious choice synthetic routes materials. The direct activation functionalization C–H bonds, bypassing intermediate functional group installation is, in abstracto, step atom economic, but numerous factors still hinder sustainability large-scale applications. In this Outlook, we highlight research areas seeking overcome challenges activation: pursuit abundant metal catalysts, avoidance static directing groups, replacement oxidants, introduction bioderived solvents. We close by examining progress made subfield aryl borylation from its origins, highly efficient precious Ir-based systems, emerging 3d catalysts. future growth field will depend on industrial uptake, thus urge researchers strive toward sustainable activation.

Язык: Английский

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C–H activation

Nature Reviews Methods Primers, Год журнала: 2021, Номер 1(1)

Опубликована: Июнь 17, 2021

Язык: Английский

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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Chemical Reviews, Год журнала: 2021, Номер 121(16), С. 9816 - 9872

Опубликована: Июль 7, 2021

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from chemistry methods. However, achieving this requires confluence coaction of expertise in computer science physical sciences. This Review is written for new experienced researchers working at the intersection both fields. We first provide concise tutorials machine methods, showing how involving can be achieved. follow with critical review noteworthy applications that demonstrate used together insightful (and useful) predictions molecular materials modeling, retrosyntheses, catalysis, drug design.

Язык: Английский

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Artificial Intelligence in Chemistry: Current Trends and Future Directions

Journal of Chemical Information and Modeling, Год журнала: 2021, Номер 61(7), С. 3197 - 3212

Опубликована: Июль 15, 2021

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution AI-related publications last two decades using CAS Content Collection. volume both journal patent have increased dramatically, especially since 2015. Study over various research areas revealed that analytical biochemistry are integrating AI greatest extent with highest rates. We also investigated trends interdisciplinary identified frequently occurring combinations publications. Furthermore, topic analyses were conducted for illustrate emerging associations certain topics. Notable disciplines then evaluated presented highlight use cases. Finally, occurrence different classes substances their roles quantified, further detailing popularity adoption life sciences chemistry. summary, Review offers a broad overview how progressed fields aims provide an understanding its future directions.

Язык: Английский

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Machine Learning for Chemical Reactivity: The Importance of Failed Experiments

Angewandte Chemie International Edition, Год журнала: 2022, Номер 61(29)

Опубликована: Май 5, 2022

Abstract Assessing the outcomes of chemical reactions in a quantitative fashion has been cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data‐driven modelling bears an enormous potential to streamline this process. However, such predictive models require significant quantities high‐quality data, availability which is limited: Main reasons for include experimental errors and, importantly, human biases regarding experiment selection and result reporting. In series case studies, we investigate impact these drawing general conclusions from reaction revealing utmost importance “negative” examples. Eventually, studies into data expansion approaches showcase directions circumvent limitations—and demonstrate perspectives towards long‐term quality enhancement chemistry.

Язык: Английский

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Automation and computer-assisted planning for chemical synthesis

Nature Reviews Methods Primers, Год журнала: 2021, Номер 1(1)

Опубликована: Март 18, 2021

Язык: Английский

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Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources

Journal of the American Chemical Society, Год журнала: 2022, Номер 144(2), С. 1045 - 1055

Опубликована: Янв. 5, 2022

Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)–C(sp3) bond formation. While many of these methods typically employ aryl bromides the C(sp2) coupling partner, variety aliphatic radical sources have been investigated. In principle, reactions enable access to same product scaffolds, but it can be hard discern which method because nonstandardized sets are used in scope evaluation. Herein, we report Ni/photoredox-catalyzed (deutero)methylation and alkylation halides where benzaldehyde di(alkyl) acetals serve alcohol-derived sources. Reaction development, mechanistic studies, late-stage derivatization biologically relevant chloride, fenofibrate, presented. Then, describe integration data science techniques, including DFT featurization, dimensionality reduction, hierarchical clustering, delineate diverse succinct collection that is representative chemical space substrate class. By superimposing examples from published on this space, identify areas sparse coverage high versus low average yields, enabling comparisons between prior art new method. Additionally, demonstrate systematically selected quantify population-wide reactivity trends reveal possible functional group incompatibility with supervised machine learning.

Язык: Английский

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Taking the leap between analytical chemistry and artificial intelligence: A tutorial review

Analytica Chimica Acta, Год журнала: 2021, Номер 1161, С. 338403 - 338403

Опубликована: Март 15, 2021

Язык: Английский

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Machine-Learning-Guided Discovery of ¹⁹F MRI Agents Enabled by Automated Copolymer Synthesis

Journal of the American Chemical Society, Год журнала: 2021, Номер 143(42), С. 17677 - 17689

Опубликована: Окт. 12, 2021

Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations monomers into a statistical copolymer overwhelms synthesis and characterization technology limits ability to systematically study structure–property relationships. To tackle this challenge in context 19F magnetic resonance imaging (MRI) agents, we pursued computer-guided materials discovery approach that combines synergistic innovations automated flow machine learning (ML) method development. A software-controlled, continuous platform was developed enable iterative experimental–computational cycles resulted 397 unique compositions within six-variable compositional space. nonintuitive design criteria identified ML, which were accomplished exploring <0.9% overall space, lead identification >10 outperformed state-of-the-art materials.

Язык: Английский

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