Atomic Simulation

Atomic simulation, using computational methods to model the behavior of atoms and molecules, aims to predict material properties and understand complex interactions at the atomic level. Current research heavily utilizes machine learning, employing graph neural networks (like EquiformerV2, DimeNet++, and GemNet) and diffusion models to create accurate and efficient interatomic potentials, overcoming challenges like systematic softening and improving the handling of diverse materials and complex structures. These advancements are significantly impacting materials science, drug discovery, and catalysis by enabling faster and more accurate predictions than traditional methods, accelerating the design and development of new materials and molecules.