In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back.

Autor: Aldossary A; Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada., Campos-Gonzalez-Angulo JA; Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada., Pablo-García S; Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada.; Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada., Leong SX; Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada., Rajaonson EM; Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada.; Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada., Thiede L; Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada.; Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada., Tom G; Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada.; Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada., Wang A; Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada., Avagliano D; Chimie ParisTech, PSL University, CNRS, Institute of Chemistry for Life and Health Sciences (iCLeHS UMR 8060), Paris, F-75005, France., Aspuru-Guzik A; Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada.; Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada.; Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada.; Department of Materials Science & Engineering, University of Toronto, 184 College St., Toronto, ON, M5S 3E4, Canada.; Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St., Toronto, ON, M5S 3E5, Canada.; Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 66118 University Ave., Toronto, M5G 1M1, Canada.; Acceleration Consortium, 80 St George St, Toronto, M5S 3H6, Canada.
Jazyk: angličtina
Zdroj: Advanced materials (Deerfield Beach, Fla.) [Adv Mater] 2024 Jul; Vol. 36 (30), pp. e2402369. Date of Electronic Publication: 2024 May 25.
DOI: 10.1002/adma.202402369
Abstrakt: Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
(© 2024 Wiley‐VCH GmbH.)
Databáze: MEDLINE
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