维数之咒
可见的
不变(物理)
统计物理学
量子
化学
势能
极限(数学)
功能(生物学)
势能面
光学(聚焦)
从头算
计算机科学
计算化学
量子力学
人工智能
物理
数学
数学分析
有机化学
进化生物学
光学
生物
作者
Sergei Manzhos,Tucker Carrington
出处
期刊:Chemical Reviews
[American Chemical Society]
日期:2020-10-06
卷期号:121 (16): 10187-10217
被引量:174
标识
DOI:10.1021/acs.chemrev.0c00665
摘要
We review progress in neural network (NN)-based methods for the construction of interatomic potentials from discrete samples (such as ab initio energies) for applications in classical and quantum dynamics including reaction dynamics and computational spectroscopy. The main focus is on methods for building molecular potential energy surfaces (PES) in internal coordinates that explicitly include all many-body contributions, even though some of the methods we review limit the degree of coupling, due either to a desire to limit computational cost or to limited data. Explicit and direct treatment of all many-body contributions is only practical for sufficiently small molecules, which are therefore our primary focus. This includes small molecules on surfaces. We consider direct, single NN PES fitting as well as more complex methods that impose structure (such as a multibody representation) on the PES function, either through the architecture of one NN or by using multiple NNs. We show how NNs are effective in building representations with low-dimensional functions including dimensionality reduction. We consider NN-based approaches to build PESs in the sums-of-product form important for quantum dynamics, ways to treat symmetry, and issues related to sampling data distributions and the relation between PES errors and errors in observables. We highlight combinations of NNs with other ideas such as permutationally invariant polynomials or sums of environment-dependent atomic contributions, which have recently emerged as powerful tools for building highly accurate PESs for relatively large molecular and reactive systems.
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