Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning

TL;DR

The paper introduces SAGE, a Bayesian algorithm that fuses diverse data streams to learn complex material relationships, enhancing materials discovery and mechanistic understanding.

Contribution

SAGE is a novel fully Bayesian coregionalization method that integrates multimodal data to model synthesis-process-structure-property relationships in materials science.

Findings

Successfully merges multiple data sources.

Provides probabilistic understanding of relationships.

Accelerates materials discovery processes.

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 to merge knowledge across data sources to learn synthesis-process-structure-property relationships. SAGE outputs a probabilistic posterior for the relationships including the most likely relationships given the data.