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Autonomous discovery of new structure-plausibility laws for explainable and rapid crystal diagnosis and screening

arXiv 2609.01209 ↗First posted 1 Sep 2026cond-mat.mtrl-sci cs.AIcited by 0

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Abstract (arXiv)

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 expensive assessment is therefore the bottleneck, yet most screens test little beyond atomic overlap and give no chemical reason for failure. Here, our agents generate, test and actively refute two million candidate laws, leaving eight Plausibility Rules for Inorganic Structures (PRIS). These laws encode five mechanisms: short-range repulsion, ionic contact and packing, electrostatic balance, bond-valence conservation and crystallographic site complexity. Experimental structures satisfy our law sets at 82--99%, but satisfy Pauling's rules 2--5 together at only 6.5%. The strictest set detects 87.9% of damaged crystal structures, whereas distance cutoffs detect only 1.6--3.2%. PRIS plausibility is linearly correlated with synthesizability, so the PRIS-derived synthesis score (PSS) explainably screens 83.7% of hard-to-synthesize structures while retaining 80.7% of experimental structures. In a property-conditioned inverse-design run, PRIS and PSS can reduce the DFT validation queue by up to 67.3% and keep 99.2% of the candidates whose DFT-validated bulk moduli reach the design target. Beyond screening, PRIS explains why GNoME remains enriched in rare low-symmetry structures and reveals how wrong-element assignments in falsified crystal reports hide behind plausible coordinates. PRIS moves screening from a pass-or-fail verdict to a chemical reason for failure, showing that autonomous agents can discover, by active refutation, physicochemical laws that guide calculations and experiments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 61 references that are in the map. First posted on arXiv 1 Sep 2026.

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References · 14

  1. 2020Shortcut Learning in Deep Neural Networkscited by 1,928
  2. 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 609
  3. 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 359
  4. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 268
  5. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 142
  6. 2024FlowMM: Generating Materials with Riemannian Flow Matchingcited by 105
  7. 2025UMA: A Family of Universal Models for Atomscited by 99
  8. 2024Space Group Constrained Crystal Generationcited by 71
  9. 2024FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributionscited by 52
  10. 2025SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Modelscited by 48
  11. 2024Space Group Informed Transformer for Crystalline Materials Generationcited by 40
  12. 2025Wyckoff Transformer: Generation of Symmetric Crystalscited by 24
2 more
  1. 2025LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Modelscited by 20
  2. 2025LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modelingcited by 4

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