Reliability of AI/ML Computational Searches for Magnetic Materials: Databases, Validation, and Synthesizability
Abstract
The rapid growth of AI/ML methods for materials discovery has increased the importance of the quality, physical completeness, and information content of computational databases. We examine these issues for magnetic materials, where incomplete sampling of competing magnetic states, chemical disorder, and finite-temperature properties can lead to apparently accurate but physically unreliable predictions. We propose a hierarchical discovery workflow combining independent ML models with explicit electronic-structure, magnetic, dynamical, thermodynamic, kinetic, and experimental validation. To incorporate kinetic accessibility into high-throughput screening, we introduce a dimensionless synthesis-window descriptor based on the relation between the chemical order-disorder and Tammann temperatures. A cluster-expansion analysis of Fe-Co-B illustrates the importance of this criterion: although ordered Fe3CoB2 is energetically favored at T=0, its small ordering energy produces an order-disorder temperature far below room temperature, making long-range Fe/Co ordering kinetically unachievable. These results demonstrate that reliable AI/ML discovery of magnetic materials requires databases and validation procedures that contain physical information relevant to the target functionality, rather than simply larger numbers of calculated structures.
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BibTeX
@misc{antropov2026reliability,
title = {{Reliability of AI/ML Computational Searches for Magnetic Materials: Databases, Validation, and Synthesizability}},
author = {Vladimir Antropov and Subhadip Pradhan and Kirill D. Belashchenko},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.07552},
eprint = {2610.07552},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.07552},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Reliability of AI/ML Computational Searches for Magnetic Materials: Databases, Validation, and Synthesizability
AU - Vladimir Antropov
AU - Subhadip Pradhan
AU - Kirill D. Belashchenko
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2610.07552
UR - https://arxiv.org/abs/2610.07552
AB - The rapid growth of AI/ML methods for materials discovery has increased the importance of the quality, physical completeness, and information content of computational databases. We examine these issues for magnetic materials, where incomplete sampling of competing magnetic states, chemical disorder, and finite-temperature properties can lead to apparently accurate but physically unreliable predictions. We propose a hierarchical discovery workflow combining independent ML models with explicit electronic-structure, magnetic, dynamical, thermodynamic, kinetic, and experimental validation. To incorporate kinetic accessibility into high-throughput screening, we introduce a dimensionless synthesis-window descriptor based on the relation between the chemical order-disorder and Tammann temperatures. A cluster-expansion analysis of Fe-Co-B illustrates the importance of this criterion: although ordered Fe3CoB2 is energetically favored at T=0, its small ordering energy produces an order-disorder temperature far below room temperature, making long-range Fe/Co ordering kinetically unachievable. These results demonstrate that reliable AI/ML discovery of magnetic materials requires databases and validation procedures that contain physical information relevant to the target functionality, rather than simply larger numbers of calculated structures.
ER -