Comparison of intermediate-range order in GeO2 glass: molecular dynamics using machine-learning interatomic potential vs. reverse Monte Carlo fitting to experimental data
skasamatsu / GeO2_glass_NNP
更新: 2024-11-22
概要
Training data for neural network potential of amorphous GeO2, as well as resulting molecular dynamics data and structure characterization
- URL
- https://isspns-gitlab.issp.u-tokyo.ac.jp/skasamatsu/GeO2_glass_NNP
- 著者
- Kenta Matsutani, Shusuke Kasamatsu, Takeshi Usuki
- ライセンス
- CC BY 4.0
- カテゴリ
- 分子動力学, 機械学習
- 関連文献
- https://arxiv.org/abs/2409.06982
- 関連ソフトウェア
- SIMPLE-NN, homcloud, RINGS
- 連絡先
- kasamatsu__at__sci.kj.yamagata-u.ac.jp (__at__ を @ に置き換えてください。)
README
Dataset for "Comparison of intermediate-range order in GeO$_2$ glass: molecular dynamics using machine-learning interatomic potential vs. reverse Monte Carlo fitting to experimental data"
About
The data repository provides a generated dataset of "Comparison of intermediate-range order in GeO$_2$ glass: molecular dynamics using machine learning interatomic potential vs. reverse Monte Carlo fitting to experimental data" This repository provides the data, including input data for construction of neural network potential and reverse Monte Carlo analysis. For the experimental data used for reverse Monte Carlo fitting, please contact Philip Salmon.
License
CC BY 4.0
出典: https://isspns-gitlab.issp.u-tokyo.ac.jp/skasamatsu/GeO2_glass_NNP の README