Journal
CHEMICAL SOCIETY REVIEWS
Volume 51, Issue 15, Pages 6475-6573Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/d2cs00203e
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Funding
- U.S. Department of Energy (Office of Basic Energy Sciences) [DE-SC0019215]
- National Science Foundation [1955907]
- U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Science Center
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Machine learning and deep learning have greatly contributed to the development of chemistry, especially in materials design and energy applications. These algorithms have also revolutionized chemical reaction dynamics and drug design. This review provides an overview of the contributions made by classical and quantum computing enhanced machine learning algorithms in chemistry, with the aim of promoting interdisciplinary collaboration.
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.
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