Accelerating Structural Optimization through Fingerprinting Space Integration on the Potential Energy Surface.
J Phys Chem Lett
; 15(11): 3185-3190, 2024 Mar 21.
Article
em En
| MEDLINE
| ID: mdl-38478975
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 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 the local atomic environment. This method strategically guides the optimization process toward high-symmetry, low-energy structures by leveraging the intrinsic symmetry of the 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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Base de dados:
MEDLINE
Idioma:
En
Revista:
J Phys Chem Lett
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos