9th Users' Conference of IT4Innovations

Developing Machine Learning Force Fields for Transition Metal Dichalcogenides with metallic substrate Ag and Si AFM tip

30 Oct 2025, 18:36

1m

atrium (IT4Innovations)

Poster Materials Science (e.g. Computational/Theoretical/Physical Chemistry, Soft Matter, Polymer Research) Conference Dinner and Poster Session

Speakers

Dr Antonio Cammarata (Department of Control Engineering - KN:G-204, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic) Ravikant Kumar (Department of Control Engineering - KN:G-204, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic)

Description

Since ab initio molecular dynamics (AIMD) simulations are computationally very expensive to study the properties of 2D layered materials, we utilize machine learning force fields (MLFF) to reduce these high costs. This approach improves the process of force field development without significantly diminishing the accuracy of the quantum-mechanical calculations. Our study focuses on four transition metal dicalgogenides (TMDs) monolayer (MoS2, MoSe2, WS2,and WSe2) interacting with a metallic substrate silver (Ag) and a silicon (Si) atomic force microscopy (AFM) tip. The initial structure of TMDs with the Ag substrate is optimized using density functional theory (DFT) calculations, and the optimized structure is then used as a training data to develop machine learning force fields (MLFFs) . Classical molecular dynamics (MD) simulations are performed using LAMMPS to further optimize the structure with MLFFs and compute the radial distribution function (RDF) to assess the accuracy of the developed force fields.

Keywords: Ab-initio, Transition Metal Dicalcogenides, Machine Learning Force Fields, DFT.