笛卡尔坐标系
对称(几何)
人工神经网络
从头算
转化(遗传学)
Atom(片上系统)
势能
水准点(测量)
简单(哲学)
物理
统计物理学
计算机科学
经典力学
量子力学
数学
化学
几何学
人工智能
认识论
地理
嵌入式系统
哲学
基因
生物化学
大地测量学
摘要
Neural networks offer an unbiased and numerically very accurate approach to represent high-dimensional ab initio potential-energy surfaces. Once constructed, neural network potentials can provide the energies and forces many orders of magnitude faster than electronic structure calculations, and thus enable molecular dynamics simulations of large systems. However, Cartesian coordinates are not a good choice to represent the atomic positions, and a transformation to symmetry functions is required. Using simple benchmark systems, the properties of several types of symmetry functions suitable for the construction of high-dimensional neural network potential-energy surfaces are discussed in detail. The symmetry functions are general and can be applied to all types of systems such as molecules, crystalline and amorphous solids, and liquids.
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