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arXiv 2602.11548cond-mat.mtrl-sci
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利用机器学习势高效模拟二维五硅烯材料的分子动力学

Efficient molecular dynamics simulation of 2D penta-silicene materials using machine learning potentials

College of Natural Sciences, Can Tho University(前江大学自然科学学院)

Le Huu Nghia, Pham Thi Bich Thao, Truong Do Anh Kha, Vo Khuong Dien, Nguyen Thanh Tien

AI总结:

本研究利用机器学习势高效模拟二维五硅烯材料的分子动力学,验证其热力学性质及结构稳定性,为实验合成提供支持并探索潜在应用。

摘要:

机器学习互作用势(MLIPs)是一种现代计算方法,允许在达到近量子力学精度(DFT)的同时,仍能描述大规模系统在分子动力学(MD)模拟中的行为。在本研究中,我们使用DeepMD包中的MLIP和经典的Tersoff势(Tersoff.SiC势)来完整且准确地描述原子相互作用,并将其应用于五硅烯片的分子动力学模拟。结果表明,在恒定NVT和等压NPT集合中,系统的熔点(Tg)温度分别为632 K和606 K,而Tersoff.SiC势的熔点较高。此外,径向分布函数在原子间距离为2.275 Å和2.375 Å处表现出特征峰,而Tersoff.SiC势仅描述了2.375 Å的距离。此外,还利用实时机器学习对五硅烯进行了10 ps的模拟,以评估系统的结构稳定性。本研究利用高精度且经济的方法研究了具有五边形结构的二维五硅烯片的热力学性质,为实验合成提供了进一步的证据,并为该材料的潜在未来应用开辟了可能。

英文摘要:

Machine Learning Interatomic Potentials (MLIPs) are a modern computational method that allows achieving near-quantum mechanical accuracy (DFT) while still describing large-scale systems in molecular dynamics (MD) simulations. In this work, we use MLIP from DeepMD package and the classical Tersoff potential for SiC (Tersoff.SiC potential) to fully and accurately describe atomic interactions and apply them to molecular dynamics simulations of penta silicene sheet. The results show that the melting points (T$_g$) temperatures of the system in the canonical NVT and isobaric NPT sets are 632 K and 606 K, while the Tersoff.SiC potential have the high melting points, respectively. In addition, the radial distribution function exhibits characteristic peaks at interatomic distances of 2.275 Å\text{} and 2.375 Å, while the Tersoff.SiC potential only describe distance of 2.375 Å. Furthermore, penta silicene was also simulated using on-the-fly machine learning for 10 ps to evaluate the structural stability of the system. This study investigates the thermodynamic properties of two-dimensional penta silicene sheets with pentagonal structures using a high-precision, cost-effective method, contributing further evidence to support experimental synthesis and opening up potential future applications of this material.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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