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
Updated: 2024-11-22
Overview
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
- Authors
- Kenta Matsutani, Shusuke Kasamatsu, Takeshi Usuki
- License
- CC BY 4.0
- Category
- Molecular dynamics, Machine learning
- References
- https://arxiv.org/abs/2409.06982
- Related software
- SIMPLE-NN, homcloud, RINGS
- Contact
- kasamatsu__at__sci.kj.yamagata-u.ac.jp (Please replace __at__ with @.)
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
Source: https://isspns-gitlab.issp.u-tokyo.ac.jp/skasamatsu/GeO2_glass_NNP README