SAGE Code and Data

Zip includes commented Jupyter notebook with associated data files. Abstract for paper is:Autonomous materials research labs require the ability to combine and learn from diverse data streams. This is especially true for learning material synthesis-process-structure-property relationships, key to accelerating materials optimization and discovery as well as accelerating mechanistic understanding. We present the Synthesis-process-structure-property relAtionship coreGionalized lEarner (SAGE) algorithm. A fully Bayesian algorithm that uses multimodal coregionalization to merge knowledge across data sources to learn synthesis-process-structure-property relationships. SAGE outputs a probabilistic posterior including the most likely relationship given the data along with proper uncertainty quantification. Beyond autonomous systems, SAGE will allow materials researchers to unify knowledge across their lab toward making better experiment design decisions.

该压缩包包含带有注释的 Jupyter 笔记本及其相关数据文件。论文摘要如下：自主材料研究实验室需要具备结合并从多种数据流中学习的功能。这对于学习材料合成-过程-结构-性能关系至关重要，这些关系是加速材料优化和发现以及加速机制理解的关键。我们提出了合成-过程-结构-性能关系核心区域化学习器（SAGE）算法。这是一个完全贝叶斯算法，利用多模态核心区域化将知识跨数据源合并以学习合成-过程-结构-性能关系。SAGE 输出包含给定数据的最大可能性关系的概率后验，以及适当的不确定性量化。除了自主系统之外，SAGE 将允许材料研究人员统一其实验室的知识，以做出更好的实验设计决策。