The FAIR principles have transformed how computational data and workflows are shared in materials research, yet existing repositories can only serve pre-computed entries -- broad coverage is perpetually incomplete and cannot adapt to new questions on demand. To address these challenges, we present OptiMat Alloys, a large language model-powered conversational agent for multi-principal element alloy exploration built on three pillars: a living database that stores every calculation with provenance, low-barrier accessibility through a web interface requiring zero programming expertise, and built-in uncertainty quantification via cross-potential and cross-configuration validation. Coupling foundational machine learning interatomic potentials covering near-all periodic table of elements with natural-language interaction, OptiMat Alloys enables targeted, on-demand computation guided by the user's domain knowledge-extending FAIR from pre-computed repositories to on-demand knowledge generation and making computational alloy screening accessible to any materials scientist.
@article{arxiv.2604.21850,
title = {OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent},
author = {Yang Hu and Vladyslav Turlo},
journal= {arXiv preprint arXiv:2604.21850},
year = {2026}
}