ATLAS: A Foundation Neural Sampler for Amorphous Materials
Merged summary
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.
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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.