Comparison of intermediate-range order in GeO2 glass: molecular dynamics using machine-learning interatomic potential vs. reverse Monte Carlo fitting to experimental data
| About | 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 |
| DOI | |
| 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 ※Please replace __at__ with @. |
kasamatsu__at__sci.kj.yamagata-u.ac.jp |
| Note |