Interatomic Potential

Interatomic potentials are mathematical functions describing the energy of a system of atoms as a function of their positions, serving as the foundation for atomistic simulations. Current research heavily emphasizes machine learning approaches, particularly graph neural networks and other equivariant architectures, to create accurate and computationally efficient potentials, often focusing on improving data efficiency, handling long-range interactions, and ensuring robustness and generalizability across diverse materials. These advancements significantly accelerate materials discovery and design by enabling large-scale simulations previously intractable with traditional methods, impacting fields ranging from materials science to drug discovery.