Department of Chemical and Petroleum Engineering Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.
Chem Rev. 2021 Aug 25;121(16):9816-9872. doi: 10.1021/acs.chemrev.1c00107. Epub 2021 Jul 7.
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
机器学习模型有望通过极大地加速计算算法并放大计算化学方法提供的见解，对化学科学产生变革性的影响。然而，要实现这一目标，需要计算机科学和物理科学的专业知识的融合和协同作用。这篇综述面向在这两个领域交叉领域工作的新老研究人员而写。我们首先提供计算化学和机器学习方法的简明教程，展示如何获得涉及两者的见解。接下来，我们对有意义的应用进行了批判性回顾，这些应用表明了如何将计算化学和机器学习结合起来，在分子和材料建模、逆合成、催化和药物设计中提供有见地（且有用）的预测。
