ATLAS: A Foundation Neural Sampler for Amorphous Materials
ATLAS is a foundation model designed to efficiently sample and design amorphous materials, which are difficult to study due to rugged energy landscapes. It learns a diffusion process using an equivariant Graph Neural Network trained on self-generated data. This allows ATLAS to generate structures directly from a potential energy function, enabling property estimation and inverse design through reward tilting.