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

A. Gilad Kusne, Austin McDannald, Brian DeCost
11/10/2023
cs.LGcond-mat.mtrl-sci

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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.

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Cite this paper

@article{kusne2023learning,
  title  = {Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning},
  author = {A. Gilad Kusne and Austin McDannald and Brian DeCost},
  year   = {2023},
  eprint = {2311.06228},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.06228v3}
}

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