密度泛函理论软件ABACUS进展及其与深度学习算法的融合及应用

陈默涵

Progress of the ABACUS Software for Density Functional Theory and Its Integration and Applications with Deep Learning Algorithms

CHEN Mohan

图2 采用ABACUS训练的半导体势函数大模型DP-Semi，可用于模拟半导体的熔点等性质[77]

Fig.2 DPA, a large model based on deep neural networks, is used to train potential functions that can describe various semiconductor materials, where the first-principles training data all come from ABACUS calculations (a); DPA-Semi model generated through training also serves as an interatomic potential function, which can be used in various semiconductor simulations (b); and using the DPA-Semi model, the computed melting points of 19 semiconductors (Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, CdS) obtained by the direct heating method (red dots) and the two-phase method (green dots) are relatively close to the experimental melting points (c)[77]