ATLAS: A Foundation Neural Sampler for Amorphous Materials
Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample. Below the glass-transition temperature, conventional molecular dynamics and Monte Carlo become inefficient because equilibration relies on rare barrier-crossing events, while data-driven generative models are constrained by scarce and biased reference ensembles. Here, we introduce ATLAS, an efficient sampler that learns a diffusion process to generate Boltzmann-distributed amorphous structures directly from a target energy function. Parameterized by an equivariant graph neural network, ATLAS generalizes across system size, temperature, and c...
| Authors | Mouyang Cheng, Denis Blessing, Botao Yu et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 21 July 2026 |
| Last updated at source | 10 October 2026 |
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