OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent
The FAIR principles have transformed how computational materials data and workflows are shared, yet existing repositories can only serve pre-computed entries — their coverage is perpetually incomplete and cannot adapt to new questions on demand. We built OptiMat Alloys, a large-language-model-powered conversational agent for multi-principal element alloy exploration, on three pillars: a living database that stores every calculation with provenance, low-barrier accessibility through a web interface that requires zero programming expertise, and built-in uncertainty quantification via cross-potential and cross-configuration validation. By coupling foundational machine-learning interatomic potentials that span nearly the entire periodic table with natural-language interaction, the agent enables targeted, on-demand computation guided by the user's own domain knowledge — extending FAIR from pre-computed repositories to on-demand knowledge generation, and making computational alloy screening accessible to any materials scientist.
Authors
- Vladyslav Turlo (ORCID: https://orcid.org/0000-0001-8120-4643)
- Yang Hu
Institutions
- Swiss Federal Laboratories for Materials Science and Technology (CH)
Publication Details
- Journal
- arXiv (Cornell University)
- Published
- 2026-08-14
- DOI
- https://doi.org/10.24435/materialscloud:e0-zp
- Primary Topic
- Machine Learning in Materials Science
- Type
- preprint
Funders
- National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials