Application of Deep-Learning Potential in Simulating the Structural and Physical Characteristics of Platinum  

作　　者：Keyuan Chen Xingkao Zhang Li Ma Jueyi Ye Qi Qiu Haoxiang Zhang Ju Rong Yudong Sui Xiaohua Yu Jing Feng 

机构地区：[1]Faculty of Materials Science and Engineering,Kunming University of Science and Technology,Kunming,650093,China [2]Yunnan Key Laboratory of Integrated Computational Materials Engineering for Advanced Light Metals,Kunming,650093,China

出　　处：《Computers, Materials & Continua》2025年第4期685-700,共16页计算机、材料和连续体(英文)

基　　金：supported by the Kunming University of Science and Technology“Double First-Class”Joint Special Project(Grant No.202101BE070001-011);Yunnan Fundamental Research Projects(202201BE070001-008,202201AT070192);National Natural Science Foundation of China(52402077);Shaanxi Province Key R&D Program(2021ZDLGY11-08);Open Project of Shaanxi Laboratory(2021SXsyS01-05);Open Project of Yunnan Precious Metals Laboratory(YPML-2023050240);National Natural Science Foundation of China(Grant No.52462009);Yunnan Fundamental Research Projects(Grant Nos.202201AT070192,202101BE070001-011).

摘　　要：The deep potential(DP)is an innovative approach based on deep learning that uses ab initio calculation data derived from density functional theory(DFT),to create high-accuracy potential functions for various materials.Platinum(Pt)is a rare metal with significant potential in energy and catalytic applications,However,there are challenges in accurately capturing its physical properties due to high experimental costs and the limitations of traditional empirical methods.This study employs deep learning methods to construct high-precision potential models for single-element systems of Pt and validates their predictive performance in complex environments.The newly developed DP is highly consistent with DFT results in predicting the stable phases,lattice constants,surface energies,and phonons dispersion relations of Pt,demonstrating outstanding quantum-level accuracy.Additionally,the complex phase transitions and domain formations of Pt are extensively and quantitatively analyzed.Molecular dynamic simulations utilizing the DP approach show that Pt’s face-centered cubic(FCC)structure undergoes a phase transition from solid to liquid at its melting point of 1986 K,this is in close agreement with the experimental value of 2041.5 K.Increased temperature enhances the diffusion of Pt atoms,with a self-diffusion coefficient of 1.17×10-11 m2/s at the melting point,comparable to that of other FCC metals.This result can be utilized for the precise analysis of the fundamental properties of the rare metal Pt at the microscopic scale,and it facilitates the development of binary or multi-component deep potential models that include Pt.

关 键 词：Deep learning ab initio calculations phase transitions molecular dynamics simulations 

分 类 号：O469[理学—凝聚态物理]