Author

Sergei V. Kalinin

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Preprint Sep 2026

ALIGNN 2.0: A Unified Line-Graph Neural Network Framework for Materials Screening, Force Fields, Inverse Design, Spectroscopy, and Microscopy

Graph neural networks are central to materials property prediction and machine-learning interatomic potentials, yet their reliance on specialized graph libraries hampers portability and reproducibility, and property and force-field models have historically required separate graph pipelines. We present ALIGNN 2.0, a dep...

Jaehyung Lee, C. R. Campbell, Akshaya Ajith et al. · 0 citations

Preprint Aug 2026

SPEAR: Structure Property Explainability with Attention Regularization

SPEAR (Structure Property Explainability with Attention Regularization) provides a principled training constraint for explainable structure property regression, yielding mechanistically meaningful explanations without sacrificing predictive performance.

Aditya Raghavan, Utkarsh Pratiush, Dalton A. Pearl et al. · 0 citations

Open access Jul 2026

Uncertainty-aware prediction of thermal expansion in complex oxides via LLM-curated literature data

An integrated workflow that combines document-grounded large language model (LLM) literature mining, human verification, and probabilistic modeling to enable uncertainty-aware prediction of the coefficient of thermal expansion (CTE) in complex oxides is introduced.

J. Marshall, M. Alexander, V. Garcia et al. · APL Machine Learning · 0 citations