Programme

P.48 -- Posters, VCTP-51

Date: Thursday, 6 August 2026

Time: 09:30 - 10:30

Investigating the Structure of Two-Dimensional Penta-Silicene via Machine Learning and Classical MD

Nguyen Thanh Tien(1), Le Huu Nghia(1), and Pham Thi Bich Thao(1)

(1) College of Natural Sciences, Can Tho University, Can Tho City, Vietnam

In this work, we use MLIP from the 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 (Tg) temperatures of the system in the canonical NVT and isobaric NPT sets are 632 K and 606 K, while the Tersoff.SiC has a high melting point. In addition, the radial distribution function exhibits characteristic peaks at interatomic distances of 2.27 Å and 2.37 Å, while the Tersoff.SiC potential only describes the distance of 2.37 Å. Furthermore, penta silicene was simulated using on-the-fly machine learning for 10 ps to evaluate the system's structural stability. 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. Machine Learning Interatomic Potentials (MLIPs) are a modern computational method that enables near-quantum-mechanical accuracy (DFT) while still describing large-scale systems in molecular dynamics (MD) simulations.

Presenter: Nguyen Thanh Tien