Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in their electronic ground state, the inclusion of light into the processes leads to electronically excited states and gives rise to several new challenges. Here, we survey recent advances for excited-state dynamics based on machine learning. In doing so, we highlight successes, pitfalls, challenges and future avenues for machine learning approaches for light-induced molecular processes.

中文翻译：

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机器学习和激发态分子动力学

机器学习在量子化学研究领域中的应用越来越多。虽然大多数方法都以研究电子基态的化学系统为目标，但将光包含在过程中会导致电子激发态，并带来了一些新的挑战。在这里，我们调查了基于机器学习的激发态动力学的最新进展。在此过程中，我们重点介绍了光诱导分子过程的机器学习方法的成功，陷阱，挑战和未来途径。