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abICS

abICS is a software framework for training a machine learning model to reproduce first-principles calculations 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, neural network potentials implemented in aenet can be used as machine learning models. abICS also provides interfaces for Quantum Espresso, VASP, aenet, and OpenMX.

Information

Official site	https://www.pasums.issp.u-tokyo.ac.jp/abics/en/about
Openness	★★★
Manual	https://www.pasums.issp.u-tokyo.ac.jp/abics/en/doc/manual
Download	https://github.com/issp-center-dev/abICS/releases
License	GPL v3
Core Developers	ver 2.0 – Shusuke Kasamatsu (Yamagata University) – Yuichi Motoyama (Institute for Solid State Physics, Univ. of Tokyo) – Tatsumi Aoyama (Institute for Solid State Physics, Univ. of Tokyo) – Kazuyoshi Yoshimi (Institute for Solid State Physics, Univ. of Tokyo) – Osamu Sugino (Institute for Solid State Physics, Univ. of Tokyo) ver. 1.0 – 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)
Availability	– python(≥3.7) – numpy – scipy – toml (for parsing input files) – mpi4py (for parallel tempering) – pymatgen (for parsing vasp I/O) – qe-tools (for parsing QE I/O)
Related Papers	Shusuke Kasamatsu, Yuichi Motoyama, Kazuyoshi Yoshimi & Tatsumi Aoyama (2023) “Configuration sampling in multi-component multi-sublattice systems enabled by ab initio Configuration sampling toolkit (abICS)” Science and<span> Technology of Advanced Materials: Methods, DOI: [https://doi.org/10.1080/27660400.2023.2284128](10.1080/27660400.2023.2284128)
Related Sites	MateriApps: https://ma.issp.u-tokyo.ac.jp/app/2364