Memory restores hidden states for better atom-by-atom crystal evolution
AtomWorld-Mem: Memory-Restored World States for Long-Horizon Atomistic Evolution
Artificial Intelligence
Summary
Predicting how materials like crystals change over time is hard because looking at just one snapshot of atoms doesn’t show the full story. The authors found that keeping memory of past atom arrangements helps reveal hidden information that influences future changes. They built a system called AtomWorld-Mem that remembers previous states to improve long-term predictions of how atoms move and rearrange. This system better matches real material behavior and works well even on materials it wasn’t trained on.
What this means in practice
- •For materials simulation engineers: Simulate long-term crystal behavior more accurately by restoring hidden atomistic states beyond single snapshots for better predictive material design.
- •For semiconductor manufacturing teams: Improve modeling of atomic defects and vacancy movements to optimize material processes under various temperatures and alloy compositions.
Authors
Tian Luo, Ruge Zhang, Haozhi Han, Yifrng Chen, Yunquan Zhang, Yunxin Liu, Ting Cao, Kun Li
Abstract
High-fidelity atomistic evolution over long timescales requires more than observing the current crystal configuration. Instantaneous atomistic snapshots are often incomplete: locally similar configurations can correspond to different hidden dynamical contexts, future event preferences, and waiting-time scales. We argue that this snapshot ambiguity makes long-horizon atomistic evolution fundamentally a memory-based world-state restoration problem. To address this, we introduce AtomWorld-Mem, a memory-restored atomistic world model that recovers the latent world state missing from instantaneous crystal snapshots. AtomWorld-Mem treats the evolving alloy as an AtomWorld: spatial encoders write multi-scale atomistic keyframes from dense local topology and sparse long-range defect context, while short-term event memory and long-term structural memory integrate these keyframes across time to restore a future-predictive evolutionary state. The restored state is used to prioritize legal vacancy-mediated events under single-event Kinetic Monte Carlo (KMC) constraints, while event legality, physical execution, and residence-time updates remain governed by the underlying simulator. Empirically, AtomWorld-Mem improves long-horizon atomistic progress under fixed microscopic event budgets while maintaining high-fidelity evolution across energetic, structural, and vacancy-transport observables. It further transfers zero-shot across diverse unseen alloy-temperature AtomWorlds, suggesting that the learned memory-restoration mechanism captures reusable principles of hidden-state inference rather than a system-specific local energy heuristic. These results position memory-restored world-state modeling as a promising route toward efficient, physically grounded, and transferable atomistic evolution.