AtomWorld-Mirror is a time‑aware macro‑step world model that compresses short micro‑event segments into physically reachable transitions between key atomic states, predicting sparse structural edits and accumulated physical time through latent macro‑step dynamics. By enforcing local reachability, inventory conservation, and continuous‑time consistency, the model replaces explicit micro‑event replay with macro‑step inference, enabling substantially faster long‑term materials evolution simulations. Across five atomic systems—including Cu‑rich RPV steel irradiation aging, Cu‑Zr metallic glass, and Li₃N‑based anti‑perovskite solid electrolyte—the approach achieves speed‑ups of 10³ to 10⁴ times over traditional event‑by‑event simulation.
By Ziming Pan, Ruge Zhang, Haozhi Han, Junkai Zhou, Xingyuan Chen, Yifeng Chen, Yunquan Zhang, Ting Cao, Yunxin Liu, Kun Li
AtomWorld-Mem is a memory‑restored atomistic world model that reconstructs hidden world states from incomplete crystal snapshots, enabling more accurate long‑horizon atomistic evolution. It uses spatial encoders to capture multi‑scale keyframes and integrates short‑term event memory with long‑term structural memory to predict future states. The restored state guides vacancy‑mediated events in kinetic Monte Carlo simulations, improving progress under fixed event budgets while preserving fidelity across energetic, structural, and transport observables, and it transfers zero‑shot across unseen alloy‑temperature scenarios.
By Tian Luo, Ruge Zhang, Haozhi Han, Yifrng Chen, Yunquan Zhang, Yunxin Liu, Ting Cao, Kun Li
ASPIRE (Atomistic Saddle-Point Inference with Refinement for Events) is a new framework that predicts multiple saddle-point candidates from a single atomic environment and refines them using Dimer searches on the original interatomic potential. Its equivariant set predictor, Ev-Quiformer, generates proposals via geometry-conditioned scalar‑vector event slots and decodes them into full atomic displacement fields. The authors provide two new datasets—BCCFE4VACAV‑4000 and BCCFE‑1TO4VAC—and demonstrate that ASPIRE achieves 77.20 % reference‑event coverage, outperforming a conventional Dimer baseline while halving the number of force evaluations and reducing wall‑time per configuration from 478.8 s to 176.3 s.
By Yucheng Zhao, Quanyou Zhang, Shaoxiang Qin, Haixuan Xu, Xiongye Xiao
arXiv:2607. 19198v1 Announce Type: cross Abstract: Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample.
By Mouyang Cheng, Denis Blessing, Botao Yu, Gerhard Neumann, Mingda Li, Carles Domingo-Enrich, Yuanqi Du
arXiv:2505. 12650v2 Announce Type: replace-cross Abstract: Reconstructing atomistic crystal structures from a single noisy STEM projection is an ill-posed inverse problem: multiple lattices can explain similar contrast, and purely feed-forward models cannot verify physical validity.
By Yaotian Yang, Yiwen Tang, Yizhe Chen, Xiao Chen, Jiangjie Qiu, Hao Xiong, Haoyu Yin, Zhiyao Luo, Yifei Zhang, Sijia Tao, Wentao Li, Qinghua Zhang, Yuqiang Li, Wanli Ouyang, Bin Zhao, Xiaonan Wang, Fei Wei
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. Th...