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.

分子中电子激发和相关过程的理论模拟对于基础研究和技术创新是必不可少的。然而，众所周知，使用量子力学方法进行此类模拟具有挑战性。机器学习的进步为辅助分子激发态模拟开辟了许多新途径。在这篇评论中，我们跟踪了这些进展，评估了当前的技术水平并强调了未来需要解决的关键问题。我们概述了机器学习在激发态研究中的广泛应用，包括预测分子特性、改进用于计算激发态特性的量子力学方法以及寻找新材料。机器学习方法可以帮助我们了解影响照片处理的隐藏因素，