段老师在SCI期刊 Comp Mater Sci上发表论文

段老师在SCI期刊 Computational Materials Science 上发表题目为“Lattice inversion potential with neural network corrections for metallic systems”。段老师为论文通讯作者。

该文章基本信息如下：

  • 标题：Lattice inversion potential with neural network corrections for metallic systems

  • 作者：Feifeng Wu, Xianbao Duan*, Ping Qian, Hang Min, Yanwei Wen, Rong Chen, Yunkun Zhao, Bin Shan*

  • 摘要：By combining the lattice inversion method and the back-propagation neural network (BPNN), we have developed a neural network-based lattice inversion potential (NN-LIP). The lattice inversion method is used to describe the pairwise interaction, and the BPNN is used to describe the many-body correction term. NN-LIP has been applied successfully to six representative noble metal systems, including Au, Ag, Pd, Pt, Ir, and Rh. The results show that NN-LIP can reproduce the results calculated by first-principles accurately, including binding energy, lattice constant, elastic constant and a series of energy curves of different lattice structured metals, greatly expanding the applicability of lattice inversion potentials. Furthermore, compared to pure machine learning potential, NN-LIP exhibits better robustness and generalization in regions not covered by the dataset used for training, which is due to the use of pairwise potential as the skeleton. NN-LIP provides a new theoretical framework for constructing high-precision potentials.

  • 期刊：Computational Materials Science

  • 链接：https://www.sciencedirect.com/science/article/pii/S0927025622001045

NN-LIP理论模型示意图
NN_LIP_schematic_diagram.png

Computational Materials Science 影响因子在2021年影响因子为3.30，在中科院分区中属于工程技术大类3区期刊。