v1.0-alpha
Pre-release
cif2x
In recent years, the use of machine learning for predicting material properties and designing substances (known as materials informatics) has gained considerable attention.
The accuracy of machine learning depends heavily on the preparation of appropriate training data.
Therefore, the development of tools and environments for the rapid generation of training data is expected to contribute significantly to the advancement of research in materials informatics.
Cif2x is a tool that generates input files for first-principles calculations from cif files.
It constructs parts that vary depending on the type of material and computational conditions from crystal structure data, using input parameters as a template.
It is capable of generating multiple input files tailored to specific computational conditions.
Currently, it supports VASP <https://www.vasp.at>, Quantum ESPRESSO <https://www.quantum-espresso.org>, and OpenMX <http://www.openmx-square.org>,
with plans to support AkaiKKR <http://kkr.issp.u-tokyo.ac.jp> in the future.
Target applications
Quantum ESPRESSO, VASP, and OpenMX
Requirement
Python3 with pymatgen, qe-tools and other library packages
Install
- From source
python3 -m pip install DIRECTORY_OF_THE_REPOSITORYLicense
The distribution of the program package and the source codes for cif2x follow
GNU General Public License version 3
(GPL v3).
Copyright (c) <2023-> The University of Tokyo. All rights reserved.
This software was developed with the support of
"Project for Advancement of Software Usability in Materials Science"
of The Institute for Solid State Physics, The University of Tokyo.
Official page
Authors
Kazuyoshi Yoshimi (ISSP, Univ. of Tokyo),
Tatsumi Aoyama (ISSP, Univ. of Tokyo),
Yuichi Motoyama (ISSP, Univ. of Tokyo),
Masahiro Fukuda (ISSP, Univ. of Tokyo),
Kota Ido (ISSP, Univ. of Tokyo),
Tetsuya Fukushima (AIST),
Shusuke Kasamatsu (Yamagata University),
Takashi Koretsune (Tohoku University),
Taisuke Ozaki (ISSP, Univ. of Tokyo)