Paper Detail
Yang Hu, Vladyslav Turlo
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 (see demo here https://youtu.be/lQzuorkzPMc). 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.
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@article{hu2026optimat,
title = {OptiMat Alloys: A FAIR End-to-End Agent with Living Database for Computational Multi-Principal Alloy Exploration},
author = {Yang Hu and Vladyslav Turlo},
year = {2026},
abstract = {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},
url = {https://arxiv.org/abs/2604.21850},
keywords = {cond-mat.mtrl-sci},
eprint = {2604.21850},
archiveprefix = {arXiv},
}
{}