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Moller Gallery

htp-tools-dev / moller-gallery

Machine learningOthersDocuments: CC BY 4.0, Codes: GNU General Public License v3.0 or later
Updated: 2024-06-10

Overview

Moller is a program to support high-throughput computations, provided as a part of the HTP-tools package. The use of machine learning for predicting material properties and designing substances, known as materials informatics, has gained considerable attention in recent years. 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. Moller generates batch job scripts for supercomputers and clusters, allowing parallel execution of programs under a series of computational conditions, such as parameter parallelism. This repository provides sample inputs and outputs that illustrate the use of Moller.

URL
https://isspns-gitlab.issp.u-tokyo.ac.jp/htp-tools-dev/moller-gallery
Authors
Kazuyoshi Yoshimi, Tatsumi Aoyama, Yuichi Motoyama, Masahiro Fukuda, Kota Ido, Tetsuya Fukushima, Shusuke Kasamatsu, Takashi Koretsune, Taisuke Ozaki
License
Documents: CC BY 4.0, Codes: GNU General Public License v3.0 or later
Category
Machine learning, Others
Related software
Moller, https://www.pasums.issp.u-tokyo.ac.jp/htp-tools/en/ (Links to: PASUMS software catalog)
Contact
htp-tools-dev__at__issp.u-tokyo.ac.jp (Please replace __at__ with @.)

Links

🧪 Examples / Tutorial💻 Open repository📦 Download ZIP

README

About

HTP-Tools is a package which is specifically designed to support high throughput computations. It includes "moller", a scripting tool for managing large-scale computations on supercomputers and clusters, and "cif2x", which generates input files for first-principles calculations from CIF files. cif2x is compatible with VASP, Quantum ESPRESSO, OpenMX, and AkaiKKR. In addition, cif2x allows the generation of multiple input files tailored to specific computational conditions.

In this repository, we introduce tutorials with moller and cif2x. The outputs are also available as the references.

The moller+cif2x samples are contributed by Hengyu Li (ISSP, University of Tokyo).

Folder structure

  • LICENSE
  • README.md
  • samples
    • moller
      • simple
      • dsqss
        • AFH-chain
      • hphi
        • AFH-chain
          • 2S_1
          • 2S_2
      • qe
    • moller+cif2x
      • Basic_usage
        • cutoff_energy
        • kpoints
      • DFT+U

Requirement

moller: Python3 with ruamel.yaml and tabulate. To run job scripts generated by moller, GNU Parallel is required.

cif2x: Python3 with pymatgen, numpy, pandas, monty, ruamel.yaml, f90nml, qe-tools (for Quantum ESPRESSO), AkaiKKRPythonUtil (for AkaiKKR).

License

  • Documents: Creative Commons Attribution 4.0 International(CC BY 4.0)
  • Source codes: GNU General Public License v3.0 or later(GPL v3).

Official page

Contact

  • htp-tools-dev__at__issp.u-tokyo.ac.jp

please replace __at__ with @.

Source: https://isspns-gitlab.issp.u-tokyo.ac.jp/htp-tools-dev/moller-gallery README

Activity

UpdatedLast updated: 2024-06-10 ()