期刊
NATURE REVIEWS CHEMISTRY
卷 5, 期 6, 页码 388-405出版社
NATURE PORTFOLIO
DOI: 10.1038/s41570-021-00278-1
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资金
- National Natural Science Foundation of China [22003051]
- Lab project of the State Key Laboratory of Physical Chemistry of Solid Surfaces
- European Research Council (ERC) Advanced Grant SubNano [832237]
- European Research Council (ERC) [832237] Funding Source: European Research Council (ERC)
This review assesses the application of machine learning in molecular excited-state simulations and highlights key issues for the future, demonstrating the potential of machine learning in predicting molecular properties, improving quantum mechanical methods, and searching for new materials.
Theoretical simulations of electronic excitations and associated processes in molecules are indispensable for fundamental research and technological innovations. However, such simulations are notoriously challenging to perform with quantum mechanical methods. Advances in machine learning open many new avenues for assisting molecular excited-state simulations. In this Review, we track such progress, assess the current state of the art and highlight the critical issues to solve in the future. We overview a broad range of machine learning applications in excited-state research, which include the prediction of molecular properties, improvements of quantum mechanical methods for the calculations of excited-state properties and the search for new materials. Machine learning approaches can help us understand hidden factors that influence photo-processes, leading to a better control of such processes and new rules for the design of materials for optoelectronic applications.
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