Web-based graphical user interface for automated materials feature engineering for machine learning
Designing descriptors for materials and catalysts within machine learning is a complex process. In particular, material compositions can be represented using information from the periodic table. Additionally, feature engineering and feature selection add further complexity, requiring significant programming skills and knowledge of machine learning. To address this challenge, a web-based graphical user interface (GUI) for feature assignment, feature engineering, and feature selection has been developed. This GUI is designed to facilitate materials and catalysts design using machine learning, without the need for advanced programming or machine learning expertise.
Authors
- Lauren Takahashi (ORCID: https://orcid.org/0000-0001-9922-8889)
- Fernando Garcia‐Escobar (ORCID: https://orcid.org/0000-0003-0587-3372)
- Keisuke Takahashi (ORCID: https://orcid.org/0000-0002-9328-1694)
- Micke Kuwahara (ORCID: https://orcid.org/0009-0002-3242-2200)
- Toshiaki Taniike (ORCID: https://orcid.org/0000-0002-4870-837X)
- Yoshiki Hasukawa (ORCID: https://orcid.org/0009-0005-4918-9187)
Institutions
- Japan Advanced Institute of Science and Technology (JP)
- Hokkaido University (JP)
Publication Details
- Journal
- Science and Technology of Advanced Materials Methods
- Published
- 2024-12-16
- DOI
- https://doi.org/10.1080/27660400.2024.2441103
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science
- Core Research for Evolutional Science and Technology
- Precursory Research for Embryonic Science and Technology
- Exploratory Research for Advanced Technology
- JST-Mirai Program