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