Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
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| Format: | Preprint |
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2024
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| _version_ | 1866915024282320896 |
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| author | Liu, Junlan Yin, Qian He, Mengshu Zhou, Jun |
| author_facet | Liu, Junlan Yin, Qian He, Mengshu Zhou, Jun |
| contents | The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio molecular dynamics (AIMD) simulations, using the moment tensor potential (MTP) as a reference. The low root mean square errors (RMSEs) for total energy and atomic forces demonstrate the high accuracy and transferability of both the MTP and NEP. We further calculate the phonon density of states (DOS) and radial distribution function (RDF) using both machine learning potentials, comparing the results to density functional theory (DFT) calculations. While the MTP potential offers slightly higher accuracy, the NEP achieves a remarkable 41-fold increase in computational speed. These findings provide detailed microscopic insights into the dynamics and rapid Cu-ion diffusion, paving the way for future studies on Cu-based solid electrolytes and their applications in energy devices. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_10911 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties Liu, Junlan Yin, Qian He, Mengshu Zhou, Jun Materials Science Machine Learning Chemical Physics The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio molecular dynamics (AIMD) simulations, using the moment tensor potential (MTP) as a reference. The low root mean square errors (RMSEs) for total energy and atomic forces demonstrate the high accuracy and transferability of both the MTP and NEP. We further calculate the phonon density of states (DOS) and radial distribution function (RDF) using both machine learning potentials, comparing the results to density functional theory (DFT) calculations. While the MTP potential offers slightly higher accuracy, the NEP achieves a remarkable 41-fold increase in computational speed. These findings provide detailed microscopic insights into the dynamics and rapid Cu-ion diffusion, paving the way for future studies on Cu-based solid electrolytes and their applications in energy devices. |
| title | Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties |
| topic | Materials Science Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2411.10911 |