Machine Learning for Superconductor Discovery: From Data-Driven Insights to Accelerated Design

ACS Omega 2026 Open access

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Authors: Jingzi Zhang, Chengquan Zhong, Cailu Xiao, Hao Wu, Weiyan Kong, Zeyun Cai, Jianchao He, H Chen, J W Li, Kailong Hu, X G Lin, Xuan Wang
+1 more Xuesong Leng
DOI: 10.1021/acsomega.6c01100
Cited by: 1
References: 113

Abstract

superconductors. Finally, challenges such as data scarcity, limited model generalizability, and insufficient cross-scale prediction capabilities are discussed, alongside proposed future directions to advance machine learning-driven superconducting materials research.

Fields of Study & Subjects

Machine Learning in Materials Science Computational Physics and Python Applications Machine Learning and Data Classification

Publication details

Journal / source
ACS Omega
Publisher
American Chemical Society (ACS)
Volume
11
Issue
22
Pages
31853–31876
Publication date
22 May 2026
Language
EN