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arXiv · 2401.13953

Accelerating Structural Optimization through Fingerprinting Space Integration on the Potential Energy Surface

Shuo Tao·Xuecheng Shao·Li Zhu
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Abstract

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.

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Shuo Tao, Xuecheng Shao, Li Zhu. 2024-01-25. Accelerating Structural Optimization through Fingerprinting Space Integration on the Potential Energy Surface. https://arxiv.org/abs/2401.13953

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