| Autor: |
Chen, Tao, Yuan, Fengbo, Liu, Jianchuan, Geng, Huayun, Zhang, Linfeng, Wang, Han, Chen, Mohan |
| Rok vydání: |
2022 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| DOI: |
10.1103/PhysRevMaterials.7.053603 |
| Popis: |
Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-based deep potentials for Sn with pressures ranging from 0 to 50 GPa and temperatures ranging from 0 to 2000 K. In particular, we find the deep potential, which is obtained by training the ab initio data from density functional theory calculations with the state-of-the-art SCAN exchange-correlation functional, is suitable to characterize high-pressure phases of Sn. We systematically validate several structural and elastic properties of the {\alpha} (diamond structure), {\beta}, bct, and bcc structures of Sn, as well as the structural and dynamic properties of liquid Sn. The thermodynamics integration method is further utilized to compute the free energies of the {\alpha}, {\beta}, bct, and liquid phases, from which the deep potential successfully predicts the phase diagram of Sn including the existence of the triple-point that qualitatively agrees with the experiment. |
| Databáze: |
arXiv |
| Externí odkaz: |
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