High-Pressure Crystal Structure Database
Abstract
High-pressure research is a productive route to new structures and emergent properties. However, crucial high-pressure structural information remains highly fragmented across individual publications and heterogeneous computational repositories. This fragmentation creates a major bottleneck for data-driven materials design. To bridge this gap, we introduce the High-Pressure Crystal Structure Database (HPCSD), a traceable, pressure-resolved repository that integrates experimental and theoretical high-pressure structures. HPCSD is constructed from two complementary data streams: elemental high-pressure phases and a searchable configuration space of stable and metastable phases generated via CALYPSO crystal structure prediction. To ensure rigorous comparability, all retained structures underwent re-optimization under a unified density functional theory (DFT) framework , with continuous enthalpy curves systematically generated specifically for the elemental phases across their stability fields. The initial release encompasses 77,346 consistently evaluated structural entries spanning 89 elements. An analysis reveals that pressure-induced polymorphism is ubiquitous and exhibits pronounced family-dependent trends. Structural diversity is strongly influenced by an element's electronic adaptability , with the greatest structural complexity emerging at intermediate rather than highest pressures. By providing standardized, reusable, and rigorously evaluated high-pressure structural data, HPCSD establishes a robust infrastructure to accelerate experimental phase identification, facilitate cross-study thermodynamic comparisons, and support the development of machine-learning interatomic potentials and generative models for high-pressure systems.
Versions
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BibTeX
@misc{wang2026highpressure,
title = {{High-Pressure Crystal Structure Database}},
author = {Zhenyu Wang and Qingchang Wang and Junwen Duan and Heng Ge and Xiaoshan Luo and Pengyue Gao and Wei Zhang and Jian Lv and Yanchao Wang and Yanming Ma},
howpublished = {arXiv},
year = {2026},
month = may,
doi = {10.48550/arxiv.2605.14471},
eprint = {2605.14471},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.14471},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - High-Pressure Crystal Structure Database
AU - Zhenyu Wang
AU - Qingchang Wang
AU - Junwen Duan
AU - Heng Ge
AU - Xiaoshan Luo
AU - Pengyue Gao
AU - Wei Zhang
AU - Jian Lv
AU - Yanchao Wang
AU - Yanming Ma
PY - 2026
DA - 2026/05/14
PB - arXiv
DO - 10.48550/arxiv.2605.14471
UR - https://arxiv.org/abs/2605.14471
AB - High-pressure research is a productive route to new structures and emergent properties. However, crucial high-pressure structural information remains highly fragmented across individual publications and heterogeneous computational repositories. This fragmentation creates a major bottleneck for data-driven materials design. To bridge this gap, we introduce the High-Pressure Crystal Structure Database (HPCSD), a traceable, pressure-resolved repository that integrates experimental and theoretical high-pressure structures. HPCSD is constructed from two complementary data streams: elemental high-pressure phases and a searchable configuration space of stable and metastable phases generated via CALYPSO crystal structure prediction. To ensure rigorous comparability, all retained structures underwent re-optimization under a unified density functional theory (DFT) framework , with continuous enthalpy curves systematically generated specifically for the elemental phases across their stability fields. The initial release encompasses 77,346 consistently evaluated structural entries spanning 89 elements. An analysis reveals that pressure-induced polymorphism is ubiquitous and exhibits pronounced family-dependent trends. Structural diversity is strongly influenced by an element's electronic adaptability , with the greatest structural complexity emerging at intermediate rather than highest pressures. By providing standardized, reusable, and rigorously evaluated high-pressure structural data, HPCSD establishes a robust infrastructure to accelerate experimental phase identification, facilitate cross-study thermodynamic comparisons, and support the development of machine-learning interatomic potentials and generative models for high-pressure systems.
ER -