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20242026
most citedAccelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges

62 citations · 73 across the 6 of their papers we have counts for

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cs.LG2025★ 3 cited

A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures

Keqiang Yan, Montgomery Bohde, Andrii Kryvenko +10

Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLI…