Sajjan Manas, Li Junxu, Selvarajan Raja, Sureshbabu Shree Hari, Kale Sumit Suresh, Gupta Rishabh, Singh Vinit, Kais Sabre
Department of Chemistry, Purdue University, West Lafayette, IN-47907, USA.
Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, Indiana 47907, USA.
Chem Soc Rev. 2022 Aug 1;51(15):6475-6573. doi: 10.1039/d2cs00203e.
Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.
机器学习（ML）已成为一种强大的力量，用于在给定数据集中识别隐藏但相关的模式，目的是随后生成自动预测行为。近年来，可以有把握地得出结论，机器学习及其近亲深度学习（DL）在物理科学的所有领域，尤其是化学领域，带来了前所未有的发展。不仅有经典的机器学习变体，甚至那些可在近期量子硬件上训练的算法也已开发出来，并取得了有前景的成果。此类算法彻底改变了材料设计以及光伏性能、关联物质基态和激发态的电子结构计算、为化学反应动力学提供信息的力场和势能面计算、受反应活性启发的药物设计合理策略，甚至物质相的分类以及对涌现临界性的准确识别。在本综述中，我们将阐述此类主题的一个子集，并描述过去几年经典和量子计算增强的机器学习算法所做出的贡献。我们不仅将简要概述这些知名技术，还将利用统计物理洞察力突出它们的学习策略。本综述的目的不仅是促进对上述技术的阐述，还旨在推动和促进化学所有领域未来研究之间的交叉融合，这些研究可以从机器学习中受益，反过来又可能加速此类算法的发展。
