Advanced materials are needed to further next-generation technologies such as
quantum computing, carbon capture, and low-cost medical imaging. However,
advanced materials discovery is confounded by two fundamental challenges: the
challenge of a high-dimensional, complex materials search space and the
challenge of combining knowledge, i.e., data fusion across instruments and
labs. To overcome the first challenge, researchers employ knowledge of the
underlying material synthesis-structure-property relationship, as a material's
structure is often predictive of its functional property and vice versa. For
example, optimal materials often occur along composition-phase boundaries or
within specific phase regions. Additionally, knowledge of the
synthesis-structure-property relationship is fundamental to understanding
underlying physical mechanisms. However, quantifying the
synthesis-structure-property relationship requires overcoming the second
challenge. Researchers must merge knowledge gathered across instruments,
measurement modalities, and even laboratories. We present the
Synthesis-structure-property relAtionship coreGionalized lEarner (SAGE)
algorithm. A fully Bayesian algorithm that uses multimodal coregionalization to
merge knowledge across data sources to learn synthesis-structure-property
relationships.

应用贝叶斯算法的 SAGE 算法通过多模态核主成分分析将来自不同数据源的知识合并，学习合成 - 结构 - 性能关系，从而克服高维复杂材料搜索空间和知识融合的挑战。