arXiv:2610.06985v2 Announce Type: replace-cross Abstract: Atomistic foundation models screen hypothetical crystals by thresholding predicted energies, an output that states neither how likely a candi...
uFlowCSP is a MeanFlow-based crystal structure prediction model that learns the average probability‑flow velocity, enabling it to generate complete crystal structures in one to five network evaluations. It achieves inference speeds 5×–58× faster than diffusion and flow‑matching methods while matching or surpassing their performance, with a chemistry‑ and symmetry‑aware Transformer that uses canonical atom ordering and per‑token chemistry embeddings. On the MP‑20 benchmark, uFlowCSP attains comparable or higher accuracy with far fewer evaluations and significantly lower wall‑clock time, demonstrating improved accuracy per network evaluation.
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.
arXiv:2606. 28578v1 Announce Type: cross Abstract: Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the cost.
The paper introduces CrystAF, an all‑atom crystal flow‑map generation model, and evaluates where physics should be incorporated into generative crystal structure models. By applying physics‑informed post‑training, the authors improve molecular validity and crystal packing without altering sampling speed, while inference‑time corrections further refine the structures. The study demonstrates that post‑training and inference‑time physics are complementary, and that the post‑training approach transfers to other generators such as Clari‑M and MolCrystalFlow.
The paper proposes ANCHOR, a composition policy that decouples crystal structure prediction (CSP) from de novo crystal generation (DNG) by using a frozen CSP model as a fixed evaluator and training the policy with multi‑objective rewards, including adaptive novelty. ANCHOR significantly improves metrics such as MSUN and SUN compared to traditional DNG approaches, and demonstrates transferability across different CSP backbones and distillation into Crystalite‑CSP. The study shows that fine‑tuning DNG models directly on these rewards yields limited gains, whereas the ANCHOR framework better leverages the CSP prior for more efficient crystal discovery.
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.
Automated materials discovery requires evaluating large pools of candidate configurations, with density functional theory (DFT) as a major throughput bottleneck. Pretrained machine-learned interatomic...
arXiv:2605. 26179v2 Announce Type: replace-cross Abstract: Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when unexpected physics emerges, and inserting steps as intermediate results reshape the problem.
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.
arXiv:2606. 07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems.
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.