论文标题
来自双光谱成分的神经网络潜力：晶体硅的案例研究
Neural Networks Potential from the Bispectrum Component: A Case Study on Crystalline Silicon
论文作者
论文摘要
在本文中，我们介绍了用于晶体硅的机器学习力场（MLFF）的系统研究。拟合MLFF的主流方法是使用分子动力学模拟的小型局部训练集，但不可能涵盖势能表面的全局特征。为了解决这个问题，我们使用随机生成的对称晶体结构来训练更一般的Si-MLFF。此外，我们在两种不同的硅数据集对材料描述符和回归技术的不同选择中进行了实质性的基准。我们的结果表明，用双光谱系数作为描述符的神经网络电势拟合是获得准确且可转移的MLFF的可行方法。
In this article, we present a systematic study in developing machine learning force fields (MLFF) for crystalline silicon. While the main-stream approach of fitting a MLFF is to use a small and localized training sets from molecular dynamics simulation, it is unlikely to cover the global feature of the potential energy surface. To remedy this issue, we used randomly generated symmetrical crystal structures to train a more general Si-MLFF. Further, we performed substantial benchmarks among different choices of materials descriptors and regression techniques on two different sets of silicon data. Our results show that neural network potential fitting with bispectrum coefficients as the descriptor is a feasible method for obtaining accurate and transferable MLFF.
