1.2 KiB
ABSTRACT
Introduction
G. Lucas and L. Pizzagalli pointed out that the use of available empirical potentials is the largest source of errors to calculate threshold displacement energies in 3C-SiC and called for the improvement of existing potentials.
Method
Training process
VASP KSPACING param
Spin-polarized calculations were considered to account for the possible spin polarization of various defect configurations.
DPGEN
Interpolation for short range repulsion
use ZBL to fit the short range repulsion
Results
Near-equilibrium properties
Equation of state: 指的是体系能量随晶格常数的变化关系
use inelastic x-ray scattering (IXS) to measure the phonon dispersion curves
Threshold displacement energies
Defect production caused by a single PKA
The Wigner-Seitz cell analysis method was used to determine the defects number.
Discussions
DP-ZBL is also about 100 times slower than the empirical potential.
Therefore, how to efficiently construct a complete training dataset for the target problem is the critical problem that the deep learning potentials and even all the machine learning potentials need to answer.