论文标题
分子电子激发态的机器学习
Machine learning for electronically excited states of molecules
论文作者
论文摘要
分子的电子激发态是光化学，光体物理学以及光生物学的核心，并且在材料科学中也起着作用。他们的理论描述需要高度准确的量子化学计算，这在计算上昂贵。在这篇综述中，我们关注的是如何使用机器学习来加快这种激动的状态模拟的速度，而且还可以如何使用人工智能的这种分支来推动这一令人兴奋的研究领域的所有方面。讨论的机器学习对激发态的应用包括激发状态动力学模拟，吸收光谱的静态计算以及许多其他。为了将这些研究置于上下文中，我们讨论了所涉及的机器学习技术的承诺和陷阱。由于后者主要基于量子化学计算，因此我们还简要介绍了激发态的电子结构方法，非绝热动力学模拟的方法，并在机器学习中用于分子的激发态时描述了技巧和问题。
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.
