Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, D-06120 Halle (Saale), Germany.
Phys Chem Chem Phys. 2019 Mar 20;21(12):6506-6516. doi: 10.1039/c8cp05771k.
We present a practical procedure to obtain reliable and unbiased neural network based force fields for solids. Training and test sets are efficiently generated from global structural prediction runs, at the same time assuring the structural variety and importance of sampling the relevant regions of phase space. The neural networks are trained to yield not only good formation energies, but also accurate forces and stresses, which are the quantities of interest for molecular dynamics simulations. Finally, we construct, as an example, several force fields for both semiconducting and metallic elements, and prove their accuracy for a variety of structural and dynamical properties. These are then used to study the melting of bulk copper and gold.
我们提出了一种实用的方法，可以为固体获得可靠且无偏的基于神经网络的力场。训练集和测试集是从全局结构预测运行中高效生成的，同时确保了结构多样性，并对相空间的相关区域进行了重要采样。神经网络的训练目的不仅是获得良好的形成能，还要准确地预测力和应力，这是分子动力学模拟中感兴趣的量。最后，我们以半导体和金属元素为例，构建了几种力场，并证明了它们在各种结构和动力学性质方面的准确性。然后，我们使用这些力场来研究块状铜和金的熔化。
