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

【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.更多还原

【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.更多还原