Atomistic Simulation

Atomistic simulation uses computational methods to model the behavior of materials at the atomic level, aiming to predict properties and dynamics with high accuracy. Current research heavily utilizes machine learning, particularly graph neural networks (GNNs) and other equivariant models, to improve the efficiency and accuracy of these simulations, often incorporating physics-informed loss functions and transfer learning techniques to enhance generalization and reduce the need for extensive training data. These advancements are significantly impacting materials science and chemistry by enabling faster and more accurate predictions of material properties, facilitating high-throughput screening, and accelerating the design of new materials with desired characteristics.