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New AI framework predicts disordered crystal structures without site annotations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EP-Flow, a novel framework for predicting disordered crystal structures. This method utilizes an Occupancy Distribution Matrix (ODM) to represent continuous site-by-species disorder, overcoming limitations of existing generators that require site-level annotations or assume deterministic occupations. EP-Flow employs a marginal-constrained flow matching approach to learn disorder patterns and generate occupancies, fractional coordinates, and lattice parameters, achieving state-of-the-art performance on disordered crystal structure prediction benchmarks. AI

IMPACT This new method could accelerate materials discovery by enabling more accurate prediction of disordered crystal structures.

RANK_REASON The cluster contains a research paper detailing a new computational method for crystal structure prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AI framework predicts disordered crystal structures without site annotations

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The cluster contains a research paper detailing a new computational method for crystal structure prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Qiuliang Liu, Liming Wu, Qi Li, Zhonglong Peng, Chang Chen, Xiaolong Chen, Wenbing Huang, Shifeng Jin ·

    EP-Flow: Disordered Crystal Structure Prediction without Site-Level Annotations

    arXiv:2610.01315v1 Announce Type: new Abstract: Generative models have made rapid progress in ordered crystal structure prediction, yet many functional materials are intrinsically disordered, with substitutional mixing, vacancies, or interstitial species controlling their propert…

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