DFT datasets for training machine-learning potential to model lithium borosilicate glasses using DeePMD
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https://zenodo.org/record/10577558
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资源简介：
Modifying ring structures in lithium borate glasses under compression: MD simulations using a machine-learning potential
Shingo Urata, Aik Rui Tan, and Rafael Gómez-Bombarelli
Phys. Rev. Materials 8, 033602 – Published 7 March 2024
DFT_DataSets.zip inlucudes atom configurations, energies, forces, box size, atom types, atom kinds, and virial in coord.raw, energy.raw, force.raw, type.raw, type_map.raw, and virial.raw, respectively. These data were divided into 10 sets in each forder named as set.000 to set.009.
All DFT data were evaluated using PBE with a cutoff energy of 600 eV by VASP.
SiO2-B2O3-Li2O.json is the input file for training DeePMD ver1.33.
SiO2-B2O3-Li2O.json is the optimized potential model of DeePMD.
创建时间：
2024-03-07
