arXiv AI July 28, 2026 By Yaotian Yang, Yiwen Tang, Yizhe Chen, Xiao Chen, Jiangjie Qiu, Hao Xiong, Haoyu Yin, Zhiyao Luo, Yifei Zhang, Sijia Tao, Wentao Li, Qinghua Zhang, Yuqiang Li, Wanli Ouyang, Bin Zhao, Xiaonan Wang, Fei Wei

AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use

Read the original on arXiv AI →

arXiv:2505. 12650v2 Announce Type: replace-cross Abstract: Reconstructing atomistic crystal structures from a single noisy STEM projection is an ill-posed inverse problem: multiple lattices can explain similar contrast, and purely feed-forward models cannot verify physical validity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 1

CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM

arXiv:2606. 31332v1 Announce Type: new Abstract: Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity.

By Minzhang Li, Mingrui Li, Weichen Qin, Qihe Chen, Sixian Shen, Yuan Pei, Jiakai Zhang, Jingyi Yu

multimodal benchmarks

arXiv Machine Learning

Jul 8

AtomBench: A Benchmarking Framework for Generative Crystal Reconstruction Models in Conventional Superconductors

arXiv:2510. 16165v2 Announce Type: replace Abstract: A key question in benchmarking generative crystal reconstruction models is how the amount and type of crystallographic information provided to a generative model affects its ability to reconstruct atomic structures.

By Charles Rhys Campbell, Aldo H. Romero, Kamal Choudhary

benchmarks

arXiv AI

Aug 10

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.

By Germain Poloudenny, Ya\"el Fr\'egier, Arnaud Demorti\`ere

benchmarks

arXiv AI

Jun 9

Context-Aware Deep Learning for Defect Classification in Atomic-Resolution STEM

arXiv:2606. 09419v1 Announce Type: cross Abstract: Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation.

By Jiadong Dan, Cheng Zhang, Leyi Loh, Ivan Verzhbitskiy, Yuan Chen, Goki Eda, Michel Bosman, N. Duane Loh

agents multimodal

arXiv Machine Learning

Jul 31

APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems

arXiv:2607. 28553v1 Announce Type: new Abstract: Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery.

By Shentong Mo, Yatao Bian

benchmarks safety

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

Related stories

CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM

AtomBench: A Benchmarking Framework for Generative Crystal Reconstruction Models in Conventional Superconductors

ED-CSP: Crystal Structure Prediction from Electron Diffraction

Context-Aware Deep Learning for Defect Classification in Atomic-Resolution STEM

APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems

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