cond-mat.mtrl-sci · 2026-09-23 · No. 122
Materials Science, 2026-09-23.
2 new papers in cond-mat.mtrl-sci. Titles, authors,
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01 — The papers
2 entries-
01
Topology-Stratified Materials Discovery with A Flow-Based Generative Model
Jingyi Zhou, Oyshee Chowdhury, Noah Oyeniran, Chongze Hu
cond-mat.mtrl-sci · cs.AI
Accurate generation of crystal structures is the foundation to the discovery of high-performance materials for extreme-environment applications, such as aerospace, additive manufacturing, and fusion energy systems. Although generative modeling has emerged as a promising approach for crystal design, its performance remains limited by the complex crystal structures and diverse chemical compositions. In this work, we develop UFO-MGen, a...
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02
Deep Generative Crystal Structure Prediction: A Benchmark Study and a Controlled Test of Prototype Dependence
Lai Wei, Rongzhi Dong, Ying Feng, Madeline Miklos, Jianjun Hu
cond-mat.mtrl-sci · cs.LG
Deep generative models are widely reported to enable de novo crystal structure prediction (CSP), but their capability has not been measured consistently against template-based methods. We evaluate 12 representative generative CSP models, spanning latent-variable, diffusion, flow-matching, autoregressive, and manifold random-walk architectures, against TCSP 2.0 on 180 test structures and a leakage-controlled subset of 46. All methods use...
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