Crystoria: A Large-Scale Crystal Dataset Linking Thermodynamic and Dynamical Stability for Materials AI
Abstract
Large-scale crystal databases have transformed materials discovery by making millions of structures and thermodynamic properties accessible to materials informatics. Yet dynamical stability remains sparsely characterized at a comparable scale because its evaluation is substantially more computationally demanding than conventional structure and energy calculations. This mismatch creates a critical gap between the rapidly expanding crystal-structure space and the physical information available for materials research and machine learning. Here we introduce Crystoria, a large-scale crystal dataset designed to bring dynamical stability to the scale of modern materials data. We construct Crystoria by systematically curating low-energy and metastable inorganic crystals, standardizing their structural identities, and combining machine-learning-accelerated structural relaxation with phonon-based dynamical screening. The resulting dataset contains 2,649,640 crystal structures, spanning 89 elements, 219 of the 230 space groups, and primitive cells containing 1-48 atoms. Among them, 1,793,353 structures (67.68%) are classified as dynamically stable, revealing substantial variation in dynamical stability across chemical and crystallographic space. By bringing dynamical stability to the scale of millions of crystal structures, Crystoria provides a physically grounded foundation for materials artificial intelligence (AI) and enables systematic exploration of the dynamical crystal landscape.
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BibTeX
@misc{pang2026crystoria,
title = {{Crystoria: A Large-Scale Crystal Dataset Linking Thermodynamic and Dynamical Stability for Materials AI}},
author = {Ke-Xin Pang and Ze-Feng Gao and Yan Gao and Zhong-Yi Lu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.38195},
eprint = {2610.38195},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.38195},
}
RIS
TY - UNPB
TI - Crystoria: A Large-Scale Crystal Dataset Linking Thermodynamic and Dynamical Stability for Materials AI
AU - Ke-Xin Pang
AU - Ze-Feng Gao
AU - Yan Gao
AU - Zhong-Yi Lu
PY - 2026
DA - 2026/10/28
PB - arXiv
DO - 10.48550/arxiv.2610.38195
UR - https://arxiv.org/abs/2610.38195
AB - Large-scale crystal databases have transformed materials discovery by making millions of structures and thermodynamic properties accessible to materials informatics. Yet dynamical stability remains sparsely characterized at a comparable scale because its evaluation is substantially more computationally demanding than conventional structure and energy calculations. This mismatch creates a critical gap between the rapidly expanding crystal-structure space and the physical information available for materials research and machine learning. Here we introduce Crystoria, a large-scale crystal dataset designed to bring dynamical stability to the scale of modern materials data. We construct Crystoria by systematically curating low-energy and metastable inorganic crystals, standardizing their structural identities, and combining machine-learning-accelerated structural relaxation with phonon-based dynamical screening. The resulting dataset contains 2,649,640 crystal structures, spanning 89 elements, 219 of the 230 space groups, and primitive cells containing 1-48 atoms. Among them, 1,793,353 structures (67.68%) are classified as dynamically stable, revealing substantial variation in dynamical stability across chemical and crystallographic space. By bringing dynamical stability to the scale of millions of crystal structures, Crystoria provides a physically grounded foundation for materials artificial intelligence (AI) and enables systematic exploration of the dynamical crystal landscape.
ER -