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abICS

  • Openness:3 ★★★
  • Document quality: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.

Information
Last Update:2024/06/14

Official site

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

License

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.

Availability

Linux
Core Developers

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)
Target substance/model

alloys, multicomponent oxides
Physical quantities that can be computed

expectation values of physical quantities (ex. energy, disorder parameter) at finite temperatures
Methodology

extended ensemble methods
Parallelization

multi-layered MPI parallelism with parallelism among replicas and parallelism of the solvers themselves
Related App
Related keywords
Document

Shusuke Kasamatsu and Osamu Sugino, J. Phys.: Condens. Matter 31 085901 (2019)
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