abICS

公開度：3 ★★★
ドキュメント充実度：2 ★★☆

Software framework for training a machine learning model to reproduce first-principles energies and then using the model to perform configurational sampling in disordered systems. It has been developed with an emphasis on multi-component solid-state systems such as metal and oxide alloys. At present, Quantum Espresso, VASP and OpenMX can be used as first-principles energy calculators, and aenet can be used to construct neural network potentials.

基本情報
Last Update:2024/06/14

公式サイト

https://www.pasums.issp.u-tokyo.ac.jp/abics/en/

ライセンス

GNU GPL version 3
This software is developed under the support of “Project for advancement of software usability in materials science” by The Institute for Solid State Physics, UTokyo. We hope that you cite the reference given below when publishing results using this software.

利用環境	Linux
開発者	Shusuke Kasamatsu (Yamagata University) Yuichi Motoyama (Institute for Solid State Physics, Univ. of Tokyo) Kazuyoshi Yoshimi (Institute for Solid State Physics, Univ. of Tokyo) Yoshiyuki Yamamoto (Institute for Solid State Physics, Univ. of Tokyo) Osamu Sugino (Institute for Solid State Physics, Univ. of Tokyo) Taisuke Ozaki (Institute for Solid State Physics, Univ. of Tokyo)
対象となる物質・モデル	alloys, multicomponent oxides
求められる物理量	expectation values of physical quantities (ex. energy, disorder parameter) at finite temperatures
手法	extended ensemble methods
並列化対応	multi-layered MPI parallelism with parallelism among replicas and parallelism of the solvers themselves
関連アプリ	QUANTUM ESPRESSO VASP aenet (ænet, The Atomic Energy Network) OpenMX
関連キーワード	Classical Monte Carlo Method Density functional method Ising Model Markov-chain Monte Carlo mothod (MCMC) Neural network
文献	Shusuke Kasamatsu and Osamu Sugino, J. Phys.: Condens. Matter 31 085901 (2019)

基本情報 Last Update:2024/06/14

公式サイト

ライセンス

基本情報
Last Update:2024/06/14