Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany.
Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, 44780 Bochum, Germany, and Atomistic Simulations, Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, 44780 Bochum, Germany.
Phys Chem Chem Phys. 2023 May 10;25(18):12979-12989. doi: 10.1039/d2cp05976b.
Machine learning potentials (MLP) enable atomistic simulations with first-principles accuracy at a small fraction of the costs of electronic structure calculations. Most modern MLPs rely on constructing the potential energy, or a major part of it, as a sum of atomic energies, which are given as a function of the local chemical environments up to a cutoff radius. Since analytic forces are readily available, nowadays it is common practice to make use of both, reference energies and forces, for training these MLPs. This can be computationally demanding since often large systems are required to obtain structurally converged reference forces experienced by atoms in realistic condensed phase environments. In this work we show how density-functional theory calculations of molecular fragments, which are too small to provide such structurally converged forces, can be used to learn forces exhibiting excellent transferability to extended systems. The general procedure and the accuracy of the method are illustrated for metal-organic frameworks using second-generation high-dimensional neural network potentials.
机器学习潜力（MLP）可以以电子结构计算成本的一小部分实现具有第一性原理准确性的原子模拟。大多数现代 MLP 依赖于将势能构建为原子能量的和，或者将其大部分构建为原子能量的和，原子能量作为局部化学环境的函数给出，直到截止半径。由于解析力很容易获得，因此现在通常的做法是同时使用参考能量和力来训练这些 MLP。这在计算上可能是很繁琐的，因为通常需要大的系统来获得原子在实际凝聚相环境中经历的结构收敛的参考力。在这项工作中，我们展示了如何使用太小而无法提供这种结构收敛力的分子片段的密度泛函理论计算来学习表现出对扩展系统优异迁移能力的力。使用第二代高维神经网络势对该方法的一般过程和准确性进行了说明。
