Herbold Marius, Behler Jörg
Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany.
J Chem Phys. 2022 Mar 21;156(11):114106. doi: 10.1063/5.0082952.
In recent years, many types of machine learning potentials (MLPs) have been introduced, which are able to represent high-dimensional potential-energy surfaces (PESs) with close to first-principles accuracy. Most current MLPs rely on atomic energy contributions given as a function of the local chemical environments. Frequently, in addition to total energies, atomic forces are also used to construct the potentials, as they provide detailed local information about the PES. Since many systems are too large for electronic structure calculations, obtaining reliable reference forces from smaller subsystems, such as molecular fragments or clusters, can substantially simplify the construction of the training sets. Here, we propose a method to determine structurally converged molecular fragments, providing reliable atomic forces based on an analysis of the Hessian. The method, which serves as a locality test and allows us to estimate the importance of long-range interactions, is illustrated for a series of molecular model systems and the metal-organic framework MOF-5 as an example for a complex organic-inorganic hybrid material.
近年来，已经引入了多种类型的机器学习势（MLP），它们能够以接近第一性原理的精度表示高维势能面（PES）。当前大多数MLP依赖于作为局部化学环境函数给出的原子能量贡献。通常，除了总能量外，原子力也用于构建势能，因为它们提供了有关PES的详细局部信息。由于许多系统对于电子结构计算来说太大，从小的子系统（如分子片段或团簇）获得可靠的参考力可以大大简化训练集的构建。在此，我们提出一种方法来确定结构收敛的分子片段，基于对海森矩阵的分析提供可靠的原子力。该方法作为一种局部性测试，使我们能够估计长程相互作用的重要性，通过一系列分子模型系统以及作为复杂有机-无机杂化材料示例的金属有机框架MOF-5进行了说明。
