TCSP 2.0: Template Based Crystal Structure Prediction with Improved Oxidation State Prediction and Chemistry Heuristics
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| Format: | Preprint |
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2025
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| _version_ | 1866913766219710464 |
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| author | Wei, Lai Dong, Rongzhi Fu, Nihang Omee, Sadman Sadeed Hu, Jianjun |
| author_facet | Wei, Lai Dong, Rongzhi Fu, Nihang Omee, Sadman Sadeed Hu, Jianjun |
| contents | Crystal structure prediction remains a major challenge in materials science, directly impacting the discovery and development of next-generation materials. We introduce TCSP 2.0, a substantial evolution of our template-based crystal structure prediction framework that advances predictive capabilities through synergistic integration of several key techniques into its major components. Building upon TCSP 1.0's template-matching foundation, this enhanced version implements three critical innovations: (1) replacement of Pymatgen with deep learning-based BERTOS model for oxidation state prediction with superior performance, (2) implementation of sophisticated element embedding distance metrics for improved chemical similarity assessment, and (3) development of a robust majority voting mechanism for space group selection that reduces prediction uncertainty. TCSP 2.0 also expands its template base by incorporating template structures from Materials Cloud, C2DB, and GNoME databases alongside the original Materials Project repository, creating a more comprehensive structural foundation. Rigorous validation across 180 diverse test cases of the CSPBenchmark demonstrates TCSP 2.0's exceptional performance, achieving 83.89% space-group success rate and 78.33% structural similarity accuracy for top-5 predictions, substantially outperforming both its predecessor and the competing modern CSP algorithms including CSPML and EquiCSP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TCSP 2.0: Template Based Crystal Structure Prediction with Improved Oxidation State Prediction and Chemistry Heuristics Wei, Lai Dong, Rongzhi Fu, Nihang Omee, Sadman Sadeed Hu, Jianjun Materials Science Crystal structure prediction remains a major challenge in materials science, directly impacting the discovery and development of next-generation materials. We introduce TCSP 2.0, a substantial evolution of our template-based crystal structure prediction framework that advances predictive capabilities through synergistic integration of several key techniques into its major components. Building upon TCSP 1.0's template-matching foundation, this enhanced version implements three critical innovations: (1) replacement of Pymatgen with deep learning-based BERTOS model for oxidation state prediction with superior performance, (2) implementation of sophisticated element embedding distance metrics for improved chemical similarity assessment, and (3) development of a robust majority voting mechanism for space group selection that reduces prediction uncertainty. TCSP 2.0 also expands its template base by incorporating template structures from Materials Cloud, C2DB, and GNoME databases alongside the original Materials Project repository, creating a more comprehensive structural foundation. Rigorous validation across 180 diverse test cases of the CSPBenchmark demonstrates TCSP 2.0's exceptional performance, achieving 83.89% space-group success rate and 78.33% structural similarity accuracy for top-5 predictions, substantially outperforming both its predecessor and the competing modern CSP algorithms including CSPML and EquiCSP. |
| title | TCSP 2.0: Template Based Crystal Structure Prediction with Improved Oxidation State Prediction and Chemistry Heuristics |
| topic | Materials Science |
| url | https://arxiv.org/abs/2503.23183 |