Abstract
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.
NSTL
Elsevier