Atomistic System

Atomistic systems research focuses on modeling and simulating the behavior of materials and molecules at the atomic level, aiming to predict their properties and dynamics. Current research heavily utilizes machine learning, particularly graph neural networks (GNNs) and equivariant models, to accelerate simulations and improve the accuracy of predictions, often incorporating techniques like diffusion models and variational autoencoders for efficient exploration of configuration space. These advancements are significantly impacting materials science, drug discovery, and other fields by enabling faster and more accurate simulations of complex systems, leading to improved design and optimization of materials and molecules.