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  • Review Article
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Molecular excited states through a machine learning lens

Abstract

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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Fig. 1: Potential energy surfaces of molecules.
Fig. 2: Machine learning for the study of excited states.
Fig. 3: Timeline of pioneering developments in the field of machine learning for excited-state research.
Fig. 4: Machine learning spectra calculated with the nuclear ensemble approach.
Fig. 5: Machine learning for surface-hopping excited-state dynamics.
Fig. 6: Strategies for optoelectronic materials design with machine learning.

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Acknowledgements

P.O.D. acknowledges funding by the National Natural Science Foundation of China (no. 22003051) and via the Lab project of the State Key Laboratory of Physical Chemistry of Solid Surfaces. M.B. acknowledges the support of the European Research Council (ERC) Advanced Grant SubNano (grant agreement 832237). This Review is dedicated to the 100th anniversary of Xiamen University.

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Dral, P.O., Barbatti, M. Molecular excited states through a machine learning lens. Nat Rev Chem 5, 388–405 (2021). https://doi.org/10.1038/s41570-021-00278-1

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