2023, Volume 27, Issue 8
Engineering >> 2023, Volume 27, Issue 8 doi: 10.1016/j.eng.2023.04.013
Machine Learning for Chemistry: Basics and Applications
a Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Sciences of the Ministry of Education, Department of Chemistry, Fudan University, Shanghai 200433, China
b Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
c School of Chemistry and Chemical Engineering, Queen’s University Belfast, Belfast BT9 5AG, Northern Ireland
# These authors contributed equally to this work.
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Abstract
The past decade has seen a sharp increase in machine learning (ML) applications in scientific research. This review introduces the basic constituents of ML, including databases, features, and algorithms, and highlights a few important achievements in chemistry that have been aided by ML techniques. The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations. Important two-dimensional (2D) and three-dimensional (3D) features representing the chemical environment of molecules and solids are briefly introduced. Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios. Three important fields of ML in chemistry are discussed: ① retrosynthesis, in which ML predicts the likely routes of organic synthesis; ② atomic simulations, which utilize the ML potential to accelerate potential energy surface sampling; and ③ heterogeneous catalysis, in which ML assists in various aspects of catalytic design, ranging from synthetic condition optimization to reaction mechanism exploration. Finally, a prospect on future ML applications is provided.
Keywords
Machine learning ; Atomic simulation ; Catalysis ; Retrosynthesis ; Neural network potential
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