arXiv Machine Learning July 8, 2026 By Charles Rhys Campbell, Aldo H. Romero, Kamal Choudhary

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

Read the original on arXiv Machine Learning →

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.

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

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

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

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