Accelerating Molecular Dynamics with a Graph Neural Network: A Scalable Approach through E(q)C-GNN

Ab initio molecular dynamics simulations are an integral part of any electronic structure calculation to access thermal stability and perform non-adiabatic dynamics but are computationally very demanding. To enhance the computational efficiency of crucial ab initio molecular dynamics simulations, in this work, we implemented the graph neural network (GNN)-accelerated predictions for the molecular dynamics simulation of two-dimensional systems with varying atom connectivity. In this work, we developed an equivariant GNN model that employs only the time-evolved AIMD-simulated atomic coordinates for training and successfully predicts the key parameters of stable two-dimensional g-CN, WTe₂, and g-CN/WTe₂, like potential energy and kinetic energy, while also delving into the structural and thermodynamical parameters like entropy and interatomic force variation, resulting in a fluctuation level of ±3%, and the computational speed has improved by several orders of magnitude. Hence, incorporating an equivariant GNN model will serve as a viable substitute for predictions in extensive AIMD simulations of homogeneous or symmetrically periodic low-dimensional materials.