Moller Gallery
| About |
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. |
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| URL | https://isspns-gitlab.issp.u-tokyo.ac.jp/htp-tools-dev/moller-gallery |
| DOI | |
| 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 |
| References | |
| Related software | Moller, https://www.pasums.issp.u-tokyo.ac.jp/htp-tools/en/ |
| Contact ※Please replace __at__ with @. |
htp-tools-dev__at__issp.u-tokyo.ac.jp |
| Note |