Low-dimensional polytellurides are of broad interest because their flexible bonding gives rise to unusual electronic states, structural instabilities, and transport phenomena, yet their average crystal structures can obscure the local tellurium motifs that actually control these properties. The electronic character of...
Yi-Hao Wang, Thomas S. Ie, C. Halbert et al.· Chemical Science· 0 citations
Crystal structure prediction (CSP) of inorganic materials is a fundamental challenge in computational materials science, yet the field has historically lacked well‐defined benchmarks and comprehensive evaluations. We address this gap by introducing CSP180, a standardized benchmark of 180 inorganic materials, and eval...
Lai Wei, Sadman Sadeed Omee, Rong-Zhi Dong et al.· Advanced Intelligent Discove...· 0 citations
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
A three-dimensional periodic space sampling method that decomposes large nanoporous structures into local geometrical sites for combined property prediction and site-wise contribution quantification and enables the interpretation of the prediction and allows for accurate identification of significant local sites for ta...
Zhenhao Zhou, Salman Bin Kashif, Han Hao et al.· 0 citations
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