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
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-Mirror introduces a time‑aware macro‑step world model that condenses short micro‑event segments into physically reachable transitions between key atomic states. By predicting sparse structural edits and accumulated physical time through latent macro‑step dynamics, it replaces explicit micro‑event replay with efficient macro‑step inference. Experiments on five atomic systems—including Cu‑rich RPV steel, Cu‑Zr metallic glass, and Li₃N‑based solid electrolytes—show speedups of 10³ to 10⁴ times over traditional event‑by‑event simulation while maintaining structural validity and time semantics.
arXiv:2608. 14076v1 Announce Type: cross Abstract: Transition-state (TS) structures define the energetic barriers and mechanistic pathways of elementary chemical reactions, yet their identification remains computationally demanding because conventional saddle-point searches require expensive quantum-mechanical calculations.
By Kaipeng Zeng, Wenxi Zhai, Shengrui Xu, Jie Zhao, Bowen Li, Shiyue Wang, Junchi Yan, Tong Zhu
The paper introduces OrbFlow, an SE(3)-equivariant generative model that predicts Gaussian-type orbital coefficients for electron density using flow matching. By training with a two-phase trajectory curriculum, OrbFlow improves accuracy on QM9 and MD benchmarks, reducing density errors significantly compared to previous models. The predicted densities also accelerate self-consistent field procedures, cutting iterations by up to 68% and accurately reproducing dipole and quadrupole moments without additional SCF calculations.
By Chenxing Liang, Chengdong Wang, Yuchao Lin, Xiaofeng Qian, Shuiwang Ji
arXiv:2606. 03199v1 Announce Type: new Abstract: Organic crystal structure prediction (CSP) is a requirement for computational modelling of organic solids, but traditionally costs several CPU-years per molecule.
By Alston Lo, Luka Mucko, Austin H. Cheng, Andy Cai, Alastair J. A. Price, Wojciech Matusik, Al\'an Aspuru-Guzik