ATLAS: A Foundation Neural Sampler for Amorphous Materials

TL;DR - ATLAS is an equivariant diffusion-based neural sampler that generates Boltzmann-distributed amorphous structures from target energy functions. It substantially reduces sampling and inverse-design costs while generalizing across material systems, temperatures, sizes, and compositions.

• Achieves below 0.2% free-energy error on low-temperature Kob–Andersen glasses with over 500× fewer energy evaluations than parallel-tempering MCMC.
• Recovers experimental short-range-order trends in Cu–Zr and Cr–Co–Ni metallic glasses and steers structures toward target properties.
• Composition-amortized pretraining reduces inverse-design costs by several hundred-fold and supports expensive machine-learning interatomic potentials.
• An LLM-agent coupling finds a stiffness–ductility Pareto frontier for eight-element high-entropy metallic glasses within 480 oracle evaluations.