Learning material synthesis–process–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
A. Gilad Kusne, Austin McDannald and Brian DeCost
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引用次数: 0

Abstract

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 and probability to merge knowledge across data sources into a unified model of 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.

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通过数据融合学习材料合成-工艺-结构-属性关系:贝叶斯共区域化 N 维片断函数学习†...
自主材料研究实验室需要具备从不同数据流中进行组合和学习的能力。这对于学习材料合成-工艺-结构-属性关系尤其如此,而这是加速材料优化和发现以及加速机理理解的关键。我们提出了合成-过程-结构-属性关系核心化学习算法(SAGE)。这是一种全贝叶斯算法,利用多模态核心区域化和概率,将不同数据源的知识合并为一个统一的合成-过程-结构-属性关系模型。SAGE 可输出概率后验,包括数据中最有可能的关系,以及适当的不确定性量化。除了自主系统之外,SAGE 还能让材料研究人员统一整个实验室的知识,从而做出更好的实验设计决策。
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