应用于光化学和光物理领域的人工智能方法飞速发展，新技术层出不穷。我们在Cristina García-Iriepa和Marco Marazzi主编的《Theoretical and Computational Photochemistry: Fundamentals, Methods, Applications and Synergy with Experimentation》一书第六章：Machine learning methods in photochemistry and photophysics中，概述了该领域的最新发展。本章描述了机器学习（ML）的基本原理和ML在光化学和光物理中的应用，而其它主题将在本书的剩余部分中介绍。本章节的一位作者是XACS的共同发起人Pavlo O.Dral。

Surging efforts and fast progress in AI methods for photochemistry and photophysics make it difficult to track the current state of the art. We cover the recent developments in this field in the chapter on Machine learning methods in photochemistry and photophysics in Theoretical and Computational Photochemistry: Fundamentals, Methods, Applications and Synergy with Experimentation edited by Cristina García-Iriepa and Marco Marazzi. This chapter is rather self-contained and describes both fundamentals of machine learning and ML applications for photochemistry and photophysics, while other topics are introduced in the remainder of the book. One of the authors is XACS co-PI Pavlo O. Dral supervising the AI direction of XACS.

光化学和光物理人工智能领域综述性文章不断增加的数目，在一定程度上映射了该领域的飞速发展。XACS团队成员也在该领域发表了部分综述性文章：

How rapidly the field of AI for science in the field of photochemistry and photophysics is developing, can be judged by the increasing number of reviews. Just to mention reviews with contributions from the XACS team members:

• Focus on learning excited-state properties in general: Julia Westermayr, Pavlo O. Dral, Philipp Marquetand. Learning excited-state properties. In Quantum Chemistry in the Age of Machine Learning, Pavlo O. Dral, Ed. Elsevier: 2023. DOI: 10.1016/B978-0-323-90049-2.00004-4.

• Focus on excited-state dynamics: Lina Zhang, Arif Ullah, Max Pinheiro Jr, Mario Barbatti, Pavlo O. Dral. Excited-state dynamics with machine learning. In Quantum Chemistry in the Age of Machine Learning, Pavlo O. Dral, Ed. Elsevier: 2023. DOI: 10.1016/B978-0-323-90049-2.00008-1.

• Focus on ML for both theoreticians and experimentalists: Pavlo O. Dral, Mario Barbatti*, Molecular excited states through a machine learning lens. Nat. Rev. Chem. 2021, 5, 388–405. DOI: 10.1038/s41570-021-00278-1. 

本章节提供了源自不同课题组的最新独特视角，同时描述了许多技术细节和ML背景，非常适合希望将ML运用于光化学和光物理理论模拟的学者阅读。专家们也可以从中找到许多有用的信息和见解。

The new chapter gives an update and a unique perspective from several groups while bringing a valuable description of many technical details and ML background. It is highly recommended for those who want to start using ML in their theoretical simulations of photochemical and photophysical processes. The experts can also find their many useful nuggets of information and insight.

Reference:

• Jingbai Li, Morgane Vacher, Pavlo O. Dral, Steven A. Lopez. Machine learning methods in photochemistry and photophysics. In Theoretical and Computational Photochemistry: Fundamentals, Methods, Applications and Synergy with Experimentation, Cristina García-Iriepa and Marco Marazzi, Eds. Elsevier: 2023. DOI: 10.1016/B978-0-323-91738-4.00002-6.