MatterTune: an integrated, user-friendly platform for fine-tuning atomistic foundation models to accelerate materials simulation and discovery
Overview of the MatterTune framework, which enables flexible and accurate fine-tuning of pre-trained atomistic foundation models for materials science.
Authors
- Nima Shoghi (ORCID: https://orcid.org/0000-0002-6846-8721)
- Victor Fung (ORCID: https://orcid.org/0000-0002-3347-6983)
- Guoxiang Hu (ORCID: https://orcid.org/0000-0003-2942-8564)
- Pan Li (ORCID: https://orcid.org/0000-0003-3742-0845)
- Lingyu Kong (ORCID: https://orcid.org/0000-0001-5952-005X)
Institutions
- Georgia Institute of Technology (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00154d
- Citations
- 4
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 2.07
Funders
- U.S. Department of Energy
- National Energy Research Scientific Computing Center
- Office of Science