Toward Large Scale Parallelization for Molecular Dynamics of Small Chemical Systems: A Combined Parallel Tempering and Domain Decomposition Approach

Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations

Message-passing graph neural network interatomic potentials (GNN-IPs), particularly those with equivariant representations such as NequIP, are attracting significant attention due to their data efficiency and high accuracy. However, parallelizing GNN-IPs poses challenges because multiple message-passing layers complicate data communication within the spatial decomposition method, which is preferred by many molecular dynamics (MD) packages. In this article, we propose an efficient parallelization scheme compatible with GNN-IPs and develop a package, SevenNet (Scalable EquiVariance-Enabled Neural NETwork), based on the NequIP architecture. For MD simulations, SevenNet interfaces with the LAMMPS package. Through benchmark tests on a 32-GPU cluster with examples of SiO2, SevenNet achieves over 80% parallel efficiency in weak-scaling scenarios and exhibits nearly ideal strong-scaling performance as long as GPUs are fully utilized. However, the strong-scaling performance significantly declines with suboptimal GPU utilization, particularly affecting parallel efficiency in cases involving lightweight models or simulations with small numbers of atoms. We also pretrain SevenNet with a vast data set from the Materials Project (dubbed "SevenNet-0") and assess its performance on generating amorphous Si3N4 containing more than 100,000 atoms. By developing scalable GNN-IPs, this work aims to bridge the gap between advanced machine-learning models and large-scale MD simulations, offering researchers a powerful tool to explore complex material systems with high accuracy and efficiency.

A soft-core Gay-Berne model for the simulation of liquid crystals by Hamiltonian replica exchange

The Gay-Berne (GB) potential has proved highly successful in the simulation of liquid crystal phases, although it is fairly demanding in terms of resources for simulations of large (e.g., N>10(5)) systems, as increasingly required in applications. Here, we introduce a soft-core GB model, which exhibits both liquid crystal phase behavior and rapid equilibration. We show that the Hamiltonian replica exchange method, coupled with the newly introduced soft-core GB model, can effectively speed up the equilibration of a GB liquid crystal phase by frequent exchange of configurations between replicas, while still recovering the mesogenic properties of the standard GB potential.