abICS Gallery
abics-dev / abICS Gallery
Overview
abICS is a 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. Specific emphasis is placed on multi-component solid state systems such as metal and oxide alloys. The current version of abics can use neural network models implemented in aenet to be used as the machine learning model. As of this moment, abICS can also generate Quantum Espresso, VASP, and OpenMX input files for obtaining the reference training data for the machine learning model. This repository provides the sample data including input and output data for abICS.
- URL
- https://isspns-gitlab.issp.u-tokyo.ac.jp/abics-dev/abics-gallery
- Authors
- Shusuke Kasamatsu, Kazuyoshi Yoshimi, Yuichi Motoyama
- License
- Documents: CC BY 4.0, Codes: GNU General Public License v3.0 or later
- Category
- Electronic structure (solid state physics), Machine learning
- Related software
- abICS (Links to: PASUMS software catalog)
- Contact
- abICS-dev__at__issp.u-tokyo.ac.jp (Please replace __at__ with @.)
README
abICS Gallery
About
abICS is a 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. Specific emphasis is placed on multi-component solid state systems such as metal and oxide alloys. The current version of abICS can use neural network models implemented in aenet to be used as the machine learning model. As of this moment, abICS can also generate Quantum Espresso, VASP, and OpenMX input files for obtaining the reference training data for the machine learning model.
License
- Documents and data: CC BY 4.0
- Source code: GNU General Public License v3.0 or later
Contact
- abICS-dev__at__issp.u-tokyo.ac.jp
please replace __at__ by @.
Source: https://isspns-gitlab.issp.u-tokyo.ac.jp/abics-dev/abics-gallery README