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 to generate input files for first-principles calculation software. It takes crystal structure data in CIF format and input parameters as a template, and constructs the parts that vary depending on the type of material and conditions. It is capable of generating multiple input files tailored to specific computational conditions. Currently, it supports VASP, Quantum ESPRESSO, OpenMX, and AkaiKKR.
Quantum ESPRESSO, VASP, OpenMX, and AkaiKKR.
Python3 with pymatgen, qe-tools, AkaiKKRPythonUtil, and other library packages.
- From source
python3 -m pip install DIRECTORY_OF_THE_REPOSITORYThe 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.
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)
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