arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.05320physics.chem-phstat.ML
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分子电子激发态的机器学习

Machine learning for electronically excited states of molecules

Julia Westermayr, Philipp Marquetand

摘要:

分子的电子激发态是光化学、光物理学和光生物学的核心,在材料科学中也发挥着重要作用。它们的理论描述需要高度精确的量子化学计算,这是计算昂贵的。在这篇综述中,我们将重点关注机器学习如何不仅用于加速这种激发态模拟,而且如何使用人工智能的这一分支来推进这一令人兴奋的研究领域的各个方面。机器学习在激发态中的应用包括激发态动力学模拟、吸收光谱的静态计算等。为了将这些研究置于背景中,我们讨论了所涉及的机器学习技术的承诺和陷阱。由于后者主要是基于量子化学计算,我们还提供了一个简短的介绍到激发态电子结构的方法,非绝热动力学模拟的方法,并描述技巧和问题时,使用它们在机器学习的分子的激发态。

英文摘要:

Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on how machine learning is employed not only to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods, approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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