arXiv AI August 10, 2026 By Germain Poloudenny, Ya\"el Fr\'egier, Arnaud Demorti\`ere

ED-CSP: Crystal Structure Prediction from Electron Diffraction

arXiv:2608. 06448v1 Announce Type: cross Abstract: Recovering a periodic 3D crystal structure from sparse, unindexed electron diffraction (ED) observations is a challenging generative inverse problem.

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benchmarks

arXiv Machine Learning

Sep 23

Deep Generative Crystal Structure Prediction: A Benchmark Study and a Controlled Test of Prototype Dependence

The study benchmarks 12 deep generative crystal structure prediction models against the template-based TCSP 2.0 on 180 test structures, finding that template retrieval achieves the highest top‑1 success (68.3%). Most generative predictions overlap with template substitutions, and removing entire stoichiometric prototype families from training reduces accuracy by 50‑78%, indicating strong prototype dependence. Only a small subset of predictions remain after such removal, suggesting limited genuine de‑novo capability.

By Lai Wei, Rongzhi Dong, Ying Feng, Madeline Miklos, Jianjun Hu

diffusion benchmarks

arXiv Machine Learning

2d ago

Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction

arXiv:2609.39773v1 Announce Type: new Abstract: Crystal structure governs material properties, making crystal structure prediction (CSP) a fundamental problem in materials science. Generative models...

By Thomas Egg, Harry Winston Sullivan, Maya M. Martirossyan, Philipp H\"ollmer, Cheng Zeng, Adrian Roitberg, Mingjie Liu, Richard Hennig, Sapna Sarupria, Ellad B. Tadmor, Stefano Martiniani

reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 15

XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models

arXiv:2606. 14003v1 Announce Type: cross Abstract: Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science.

By Nofit Segal, Mingda Li, Benjamin Kurt Miller, Rafael G\'omez-Bombarelli

diffusion benchmarks

arXiv Machine Learning

Jun 2

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design

arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.

By Anand Babu, Rog\'erio Almeida Gouv\^ea, Gian-Marco Rignanese

diffusion reinforcement-learning multimodal

arXiv Machine Learning

Jul 10

MatBind: A Shared Embedding Space for Multimodal Materials Characterization

arXiv:2607. 08470v1 Announce Type: new Abstract: Fully characterizing a crystalline material requires integrating heterogeneous data sources -- atomic structures, diffraction patterns, electronic density of states, and natural language -- each of which captures a different facet of the same physical object.

By Le Yang (Institute for Advanced Simulations), Anoop K. Chandran (J\"ulich Supercomputing Centre, Forschungszentrum J\"ulich), Jona \"Ostreicher (Institute of Nanotechnology, Karlsruhe Institute of Technology), Evgenii Sovetkin (J\"ulich Supercomputing Centre, Forschungszentrum J\"ulich), Adrian Mirza (Helmholtz-Zentrum Berlin f\"ur Materialien und Energie, Helmholtz Institute for Polymers in Energy Applications Jena), Sebastien Bompas (Institute for Advanced Simulations), Bashir Kazimi (Institute for Advanced Simulations), Pascal Friederich (Institute of Nanotechnology, Karlsruhe Institute of Technology), Stefan Kesselheim (J\"ulich Supercomputing Centre, Forschungszentrum J\"ulich, 1. Physikalisches Institut, University of Cologne), Kevin Maik Jablonka (Helmholtz Institute for Polymers in Energy Applications Jena, Center for Energy and Environmental Chemistry Jena, Friedrich Schiller University Jena), Stefan Sandfeld (Institute for Advanced Simulations, Faculty 5 - Georesources and Materials Engineering, RWTH Aachen University)

rag multimodal safety

arXiv Machine Learning

Aug 17

SPEAR: Structure Property Explainability with Attention Regularization

arXiv:2608. 13826v1 Announce Type: cross Abstract: Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions.

By Aditya Raghavan, Utkarsh Pratiush, Dalton A. Pearl, Jade Holliman Jr, Katharine Page, Philip D Rack, Sergei V Kalinin

benchmarks safety

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Deep Generative Crystal Structure Prediction: A Benchmark Study and a Controlled Test of Prototype Dependence

Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction

XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design

MatBind: A Shared Embedding Space for Multimodal Materials Characterization

SPEAR: Structure Property Explainability with Attention Regularization