Scaling Autoregressive Models for Lattice Thermodynamics
Predicting how materials behave under realistic conditions requires understanding the statistical distribution of atomic configurations on crystal lattices, a problem central to alloy design, catalysis, and the study of phase transitions. Traditional Markov-chain Monte Carlo sampling suffers from slow convergence and critical slowing down near phase transitions, motivating the use of generative models that directly learn the thermodynamic distribution. Existing autoregressive models (ARMs), however, generate configurations in a fixed sequential order and incur high memory and training costs, limiting their applicability to realistic systems. Here, we develop a framework combining any-orde...
| Authors | Xiaochen Du, Juno Nam, Sulin Liu et al. |
|---|---|
| Primary category | cond-mat.stat-mech |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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