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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2604.20230 |
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| _version_ | 1866910156956106752 |
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| author | Wang, Xiaoyang Wang, Yinan Zhao, Wenbo Liu, Hanyu Xie, Hao Wang, Lei Wang, Han |
| author_facet | Wang, Xiaoyang Wang, Yinan Zhao, Wenbo Liu, Hanyu Xie, Hao Wang, Lei Wang, Han |
| contents | Accurate crystal structure prediction (CSP) requires accounting for finite-temperature and nuclear quantum effects, yet first-principles evaluation of the free energy surface (FES) remains prohibitive for high-throughput searches. We observe that the self-consistent harmonic approximation (SCHA) FES, as a function of nuclear centroid positions, shares the same mathematical structure as a potential-energy surface and can therefore be directly learned by a deep neural network potential. The resulting deep free energy (DF) model, constructed via a two-level concurrent-learning workflow, evaluates free energies, forces, and stresses in a single forward pass. Applied to the La-Sc-H system at 200 GPa and 300 K, DF-based CSP reproduces the stability of the experimentally observed LaH10 and LaSc2H24, and discovers an unreported thermodynamically stable clathrate hydride: P4/mmm LaScH8. Benchmarked on the LaH10 system, the DF model achieves a 1.72*10^6-fold cost reduction relative to DFT-level SSCHA. The DF framework provides a scalable route for incorporating finite-temperature and nuclear quantum effects into high-throughput crystal structure prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20230 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Crystal structure prediction with nuclear quantum and finite-temperature effects via deep free energy learning Wang, Xiaoyang Wang, Yinan Zhao, Wenbo Liu, Hanyu Xie, Hao Wang, Lei Wang, Han Materials Science Accurate crystal structure prediction (CSP) requires accounting for finite-temperature and nuclear quantum effects, yet first-principles evaluation of the free energy surface (FES) remains prohibitive for high-throughput searches. We observe that the self-consistent harmonic approximation (SCHA) FES, as a function of nuclear centroid positions, shares the same mathematical structure as a potential-energy surface and can therefore be directly learned by a deep neural network potential. The resulting deep free energy (DF) model, constructed via a two-level concurrent-learning workflow, evaluates free energies, forces, and stresses in a single forward pass. Applied to the La-Sc-H system at 200 GPa and 300 K, DF-based CSP reproduces the stability of the experimentally observed LaH10 and LaSc2H24, and discovers an unreported thermodynamically stable clathrate hydride: P4/mmm LaScH8. Benchmarked on the LaH10 system, the DF model achieves a 1.72*10^6-fold cost reduction relative to DFT-level SSCHA. The DF framework provides a scalable route for incorporating finite-temperature and nuclear quantum effects into high-throughput crystal structure prediction. |
| title | Crystal structure prediction with nuclear quantum and finite-temperature effects via deep free energy learning |
| topic | Materials Science |
| url | https://arxiv.org/abs/2604.20230 |