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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.13953 |
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| _version_ | 1866909082800095232 |
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| author | Tao, Shuo Shao, Xuecheng Zhu, Li |
| author_facet | Tao, Shuo Shao, Xuecheng Zhu, Li |
| contents | Structural optimization has been a crucial component in computational materials research, and structure predictions have relied heavily on this technique in particular. In this study, we introduce a novel method that enhances the efficiency of local optimization by integrating an extra fingerprint space into the optimization process. Our approach utilizes a mixed energy concept in the hyper potential energy surface (PES), combining real energy and a newly introduced fingerprint energy derived from the symmetry of local atomic environment. This method strategically guides the optimization process toward high-symmetry, low-energy structures by leveraging the intrinsic symmetry of atomic configurations. The effectiveness of our approach was demonstrated through structural optimizations of silicon, silicon carbide, and Lennard-Jones cluster systems. Our results show that the fingerprint space biasing technique significantly enhances the performance and probability of discovering energetically favorable, high-symmetry structures, as compared to conventional optimizations. The proposed method is anticipated to streamline the search for new materials and facilitate the discovery of novel, energetically favorable configurations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13953 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Structural Optimization through Fingerprinting Space Integration on the Potential Energy Surface Tao, Shuo Shao, Xuecheng Zhu, Li Materials Science Structural optimization has been a crucial component in computational materials research, and structure predictions have relied heavily on this technique in particular. In this study, we introduce a novel method that enhances the efficiency of local optimization by integrating an extra fingerprint space into the optimization process. Our approach utilizes a mixed energy concept in the hyper potential energy surface (PES), combining real energy and a newly introduced fingerprint energy derived from the symmetry of local atomic environment. This method strategically guides the optimization process toward high-symmetry, low-energy structures by leveraging the intrinsic symmetry of atomic configurations. The effectiveness of our approach was demonstrated through structural optimizations of silicon, silicon carbide, and Lennard-Jones cluster systems. Our results show that the fingerprint space biasing technique significantly enhances the performance and probability of discovering energetically favorable, high-symmetry structures, as compared to conventional optimizations. The proposed method is anticipated to streamline the search for new materials and facilitate the discovery of novel, energetically favorable configurations. |
| title | Accelerating Structural Optimization through Fingerprinting Space Integration on the Potential Energy Surface |
| topic | Materials Science |
| url | https://arxiv.org/abs/2401.13953 |