Müller Carolin, Sršeň Štěpán, Bachmair Brigitta, Crespo-Otero Rachel, Li Jingbai, Mausenberger Sascha, Pinheiro Max, Worth Graham, Lopez Steven A, Westermayr Julia
Computer-Chemistry-Center, Friedrich-Alexander-Universität Erlangen-Nürnberg Nägelsbachstraße 25 91052 Erlangen Germany.
Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna Währinger Straße 17 1090 Wien Austria.
Chem Sci. 2025 Sep 4. doi: 10.1039/d5sc05579b.
Exploring molecular excited states holds immense significance across organic chemistry, chemical biology, and materials science. Understanding the photophysical properties of molecular chromophores is crucial for designing nature-inspired functional molecules, with applications ranging from photosynthesis to pharmaceuticals. Non-adiabatic molecular dynamics simulations are powerful tools to investigate the photochemistry of molecules and materials, but demand extensive computing resources, especially for complex molecules and environments. To address these challenges, the integration of machine learning has emerged. Machine learning algorithms can be used to analyse vast datasets and accelerate discoveries by identifying relationships between geometrical features and ground as well as excited-state properties. However, challenges persist, including the acquisition of accurate excited-state data and managing the complexity of the data. This article provides an overview of recent and best practices in machine learning for non-adiabatic molecular dynamics, focusing on pre-processing, surface fitting, and post-processing of data.
探索分子激发态在有机化学、化学生物学和材料科学领域具有重大意义。了解分子发色团的光物理性质对于设计受自然启发的功能分子至关重要，这些分子的应用范围涵盖从光合作用到制药等领域。非绝热分子动力学模拟是研究分子和材料光化学的强大工具，但需要大量计算资源，尤其是对于复杂分子和环境而言。为应对这些挑战，机器学习的整合应运而生。机器学习算法可用于分析大量数据集，并通过识别几何特征与基态以及激发态性质之间的关系来加速发现。然而，挑战依然存在，包括获取准确的激发态数据以及管理数据的复杂性。本文概述了用于非绝热分子动力学的机器学习的最新进展和最佳实践，重点关注数据的预处理、表面拟合和后处理。
