中国物理B ›› 2025, Vol. 34 ›› Issue (12): 120702-120702.doi: 10.1088/1674-1056/ae172a

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MaterialsGalaxy: A platform fusing experimental and theoretical data in condensed matter physics

Tiannian Zhu(朱天念)1,2, Zhong Fang(方忠)1,2, Quansheng Wu(吴泉生)1,2,†, and Hongming Weng(翁红明)1,2,‡   

  1. 1 Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China
  • 收稿日期:2025-09-16 修回日期:2025-10-19 接受日期:2025-10-24 发布日期:2025-12-04
  • 通讯作者: Quansheng Wu, Hongming Weng E-mail:quansheng.wu@iphy.ac.cn;hmweng@iphy.ac.cn
  • 基金资助:
    This work was supported by the Science Center of the National Natural Science Foundation of China (Grant No. 12188101), the National Natural Science Foundation of China (Grant Nos. 12274436 and 11921004), the National Key R&D Program of China (Grant Nos. 2023YFA1607400 and 2022YFA1403800), H.W. acknowledges support from the New Cornerstone Science Foundation through the XPLORER PRIZE.

MaterialsGalaxy: A platform fusing experimental and theoretical data in condensed matter physics

Tiannian Zhu(朱天念)1,2, Zhong Fang(方忠)1,2, Quansheng Wu(吴泉生)1,2,†, and Hongming Weng(翁红明)1,2,‡   

  1. 1 Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2025-09-16 Revised:2025-10-19 Accepted:2025-10-24 Published:2025-12-04
  • Contact: Quansheng Wu, Hongming Weng E-mail:quansheng.wu@iphy.ac.cn;hmweng@iphy.ac.cn
  • Supported by:
    This work was supported by the Science Center of the National Natural Science Foundation of China (Grant No. 12188101), the National Natural Science Foundation of China (Grant Nos. 12274436 and 11921004), the National Key R&D Program of China (Grant Nos. 2023YFA1607400 and 2022YFA1403800), H.W. acknowledges support from the New Cornerstone Science Foundation through the XPLORER PRIZE.

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摘要： Modern materials science generates vast and diverse datasets from both experiments and computations, yet these multi-source, heterogeneous data often remain disconnected in isolated “silos”. Here, we introduce MaterialsGalaxy, a comprehensive platform that deeply fuses experimental and theoretical data in condensed matter physics. Its core innovation is a structure similarity-driven data fusion mechanism that quantitatively links cross-modal records—spanning diffraction, crystal growth, computations, and literature—based on their underlying atomic structures. The platform integrates artificial intelligence (AI) tools, including large language models (LLMs) for knowledge extraction, generative models for crystal structure prediction, and machine learning property predictors, to enhance data interpretation and accelerate materials discovery. We demonstrate that MaterialsGalaxy effectively integrates these disparate data sources, uncovering hidden correlations and guiding the design of novel materials. By bridging the long-standing gap between experiment and theory, MaterialsGalaxy provides a new paradigm for data-driven materials research and accelerates the discovery of advanced materials.

关键词: MaterialsGalaxy, data fusion, materials gene, materials database

Abstract: Modern materials science generates vast and diverse datasets from both experiments and computations, yet these multi-source, heterogeneous data often remain disconnected in isolated “silos”. Here, we introduce MaterialsGalaxy, a comprehensive platform that deeply fuses experimental and theoretical data in condensed matter physics. Its core innovation is a structure similarity-driven data fusion mechanism that quantitatively links cross-modal records—spanning diffraction, crystal growth, computations, and literature—based on their underlying atomic structures. The platform integrates artificial intelligence (AI) tools, including large language models (LLMs) for knowledge extraction, generative models for crystal structure prediction, and machine learning property predictors, to enhance data interpretation and accelerate materials discovery. We demonstrate that MaterialsGalaxy effectively integrates these disparate data sources, uncovering hidden correlations and guiding the design of novel materials. By bridging the long-standing gap between experiment and theory, MaterialsGalaxy provides a new paradigm for data-driven materials research and accelerates the discovery of advanced materials.

Key words: MaterialsGalaxy, data fusion, materials gene, materials database

中图分类号:  (Neural networks, fuzzy logic, artificial intelligence)

  • 07.05.Mh
61.50.Ah (Theory of crystal structure, crystal symmetry; calculations and modeling) 71.15.-m (Methods of electronic structure calculations) 61.05.cc (Theories of x-ray diffraction and scattering)