Molecular Simulation

Molecular simulation aims to computationally model the behavior of molecules and materials, providing insights into their structure, dynamics, and properties. Current research heavily utilizes machine learning, particularly graph neural networks (GNNs) and neural network potentials (NNPs), often incorporating equivariance for improved efficiency and transferability across diverse systems, to accelerate simulations and enhance accuracy. These advancements are significantly impacting fields like drug discovery, materials science, and chemical engineering by enabling faster and more accurate predictions of molecular properties and behaviors, reducing reliance on expensive experimental methods.