Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory

被引:17
作者
Kuntz, David [2 ]
Wilson, Angela K. [1 ]
机构
[1] Michigan State Univ, Dept Chem, E Lansing, MI 48824 USA
[2] Univ North Texas, Dept Chem, Denton, TX 76201 USA
关键词
analytical chemistry; artificial intelligence; computational chemistry; IUPAC Distinguished Women in Chemistry and Chemical Engineering; machine learning; theoretical chemistry; women in science; PARTIAL LEAST-SQUARES; QUANTITATIVE STRUCTURE-ACTIVITY; INITIO MOLECULAR-DYNAMICS; DENSITY-FUNCTIONAL THEORY; MANY-BODY PROBLEM; BIG-DATA; QUANTUM-CHEMISTRY; GAUSSIAN-PROCESSES; RANDOM FOREST; SELECTION;
D O I
10.1515/pac-2022-0202
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist's perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.
引用
收藏
页码:1019 / 1054
页数:36
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