arXiv：cs.AI· Ziming Pan, Ruge Zhang, Haozhi Han, Junkai Zhou, Xingyuan Chen, Yifeng Chen, Yunquan Zhang, Ting Cao, Yunxin Liu, Kun Li·· 3 小时前
AtomWorld-Mirror：面向材料动力学的宏观步世界模型
AtomWorld-Mirror: Macro-Step World Modeling of Critical Evolution Backbones for Materials Dynamics
AI 导读
研究提出 AtomWorld-Mirror，一种面向原子系统关键演化骨架的时间感知宏观步世界模型，将短微观事件段蒸馏为关键状态间物理可达的跃迁，联合预测稀疏结构编辑与累积物理时间。在 Cu 富集 RPV 钢辐照老化、Cu-Zr 金属玻璃和 Li₃N 基反钙钛矿固态电解质共五个原子系统上，宏观步推理较逐事件模拟实现 10³ 至 10⁴ 倍加速。
正文
Abstract:Atomistic simulation is a fundamental tool for studying long-term materials evolution, from diffusion and defect dynamics to interfacial reactions and fracture. Yet conventional simulators typically advance at microscopic resolution, spending substantial computation on low-impact local updates before reaching structurally consequential states, an evolutionary-resolution bottleneck that limits long-horizon simulation. We propose AtomWorld-Mirror, a time-aware macro-step world model for the critical evolution backbone of atomic systems. For Step-Wise atomistic simulation, AtomWorld-Mirror distills short micro-event segments into physically reachable transitions between key states, jointly predicting sparse structural edits and accumulated physical time through latent macro-step dynamics. Local reachability, inventory conservation, and continuous-time consistency constrain each transition. By amortizing local atomic physics into a reusable latent macro model and replacing explicit micro-event replay with macro-step inference, this formulation provides a path toward substantially faster prediction of long-term materials evolution while preserving structural validity and time semantics. Across five atomic systems, spanning Cu-rich RPV steel irradiation aging, Cu-Zr metallic glass, and Li$_3$N-based anti-perovskite solid electrolyte, macro-step inference delivers a speed up of $10^3$ to $10^4$ times over event-by-event simulation.
| Comments: | Project page: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci) |
| Cite as: | arXiv:2610.11527 [cs.AI] |
| (or arXiv:2610.11527v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11527 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ziming Pan [view email]
[v1]
Thu, 8 Oct 2026 08:56:53 UTC (2,654 KB)
来源：arXiv：cs.AI · arxiv.org