arXiv AI
CrystalJev: calibrated foundation-model probabilities decide what to compute next in materials discovery
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arXiv AI
Oct 7
CrystalJev: thinking fast and slow with atomistic foundation models for materials discovery
arXiv:2610.06985v1 Announce Type: cross Abstract: Atomistic foundation models triage millions of hypothetical materials but are used as slow simulators, their thresholded energies taken at face value...
arXiv AI
Sep 2
Autonomous discovery of new structure-plausibility laws for explainable and rapid crystal diagnosis and screening
The paper reports that autonomous agents can generate, test, and refute millions of candidate structure-plausibility laws, ultimately producing eight Plausibility Rules for Inorganic Structures (PRIS) that capture five key mechanisms of crystal stability. PRIS achieves high agreement with experimental structures (82–99%) and outperforms traditional Pauling rules, detecting damaged crystals with 87.9% accuracy and correlating strongly with synthesizability. By integrating PRIS with a synthesis score (PSS), the authors demonstrate significant reductions in expensive DFT validation and improved inverse-design efficiency, while also providing chemical explanations for failures and anomalies in crystal data.
Hugging Face Trending Papers
6d ago
Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials
Automated materials discovery requires evaluating large pools of candidate configurations, with density functional theory (DFT) as a major throughput bottleneck. Pretrained machine-learned interatomic...
Hugging Face Trending Papers
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Autonomous discovery of new structure-plausibility laws for explainable and rapid crystal diagnosis and screening
Crystal generators and tool-using agents propose structures faster than density functional theory (DFT) energy and phonon calculations or experiments can assess them. Deciding which candidates merit e...
arXiv Machine Learning
5d ago
Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials
arXiv:2610.19630v1 Announce Type: cross Abstract: Automated materials discovery requires evaluating large pools of candidate configurations, with density functional theory (DFT) as a major throughput...
arXiv AI
Sep 11
uFlowCSP: Crystal Structure Prediction using Mean flow generative models
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.