{"ID":2863875,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25186","arxiv_id":"2509.25186","title":"Guided Diffusion for the Discovery of New Superconductors","abstract":"The inverse design of materials with specific desired properties, such as high-temperature superconductivity, represents a formidable challenge in materials science due to the vastness of chemical and structural space. We present a guided diffusion framework to accelerate the discovery of novel superconductors. A DiffCSP foundation model is pretrained on the Alexandria Database and fine-tuned on 7,183 superconductors with first principles derived labels. Employing classifier-free guidance, we sample 200,000 structures, which lead to 34,027 unique candidates. A multistage screening process that combines machine learning and density functional theory (DFT) calculations to assess stability and electronic properties, identifies 773 candidates with DFT-calculated $T_\\mathrm{c}\u003e5$ K. Notably, our generative model demonstrates effective property-driven design. Our computational findings were validated against experimental synthesis and characterization performed as part of this work, which highlighted challenges in sparsely charted chemistries. This end-to-end workflow accelerates superconductor discovery while underscoring the challenge of predicting and synthesizing experimentally realizable materials.","short_abstract":"The inverse design of materials with specific desired properties, such as high-temperature superconductivity, represents a formidable challenge in materials science due to the vastness of chemical and structural space. We present a guided diffusion framework to accelerate the discovery of novel superconductors. A DiffC...","url_abs":"https://arxiv.org/abs/2509.25186","url_pdf":"https://arxiv.org/pdf/2509.25186v1","authors":"[\"Pawan Prakash\",\"Jason B. Gibson\",\"Zhongwei Li\",\"Gabriele Di Gianluca\",\"Juan Esquivel\",\"Eric Fuemmeler\",\"Benjamin Geisler\",\"Jung Soo Kim\",\"Adrian Roitberg\",\"Ellad B. Tadmor\",\"Mingjie Liu\",\"Stefano Martiniani\",\"Gregory R. Stewart\",\"James J. Hamlin\",\"Peter J. Hirschfeld\",\"Richard G. Hennig\"]","published":"2025-09-29T17:59:52Z","proceeding":"cond-mat.supr-con","tasks":"[\"cond-mat.supr-con\",\"cond-mat.mtrl-sci\",\"cs.AI\"]","methods":"[\"Diffusion Model\"]","has_code":false}