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.

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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

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preprint

OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent

Vladyslav Turlo, Yang Hu
arXiv (Cornell University)
Machine Learning in Materials Science
preprint

OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent

Vladyslav Turlo, Yang Hu
preprint en

Abstract

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.

arXiv (Cornell University)
Swiss Federal Laboratories for Materials Science and Technology (CH)
National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials
Quality Education
Machine Learning in Materials Science
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