Efficient molecular dynamics simulation of 2D penta-silicene materials using machine learning potentials
Le Huu Nghia, Pham Thi Bich Thao, Truong Do Anh Kha, Vo Khuong Dien, and Nguyen Thanh Tien

TL;DR
This paper demonstrates the use of machine learning interatomic potentials for efficient and accurate molecular dynamics simulations of 2D penta-silicene, revealing thermodynamic properties and structural stability.
Contribution
It introduces a novel application of MLIPs to simulate penta silicene, providing detailed thermodynamic and structural insights with high accuracy and computational efficiency.
Findings
Melting points of 632 K (NVT) and 606 K (NPT) identified.
Radial distribution function peaks at 2.275 Å and 2.375 Å observed.
Structural stability confirmed through 10 ps on-the-fly ML simulations.
Abstract
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) 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 \AA \text{} and 2.375 \AA, while the Tersoff.SiC potential only describe distance of 2.375 \AA.…
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Taxonomy
TopicsGraphene research and applications · Thermal properties of materials · Advanced Physical and Chemical Molecular Interactions
