add SiC/Deep learning inter-atomic potential for irradiation damage in 3C-SiC

SiC/Deep learning inter-atomic potential for irradiation damage in 3C-SiC.md

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+# 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.
+
+# Conclusions
+