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

Published

Author(s)

A. Gilad Kusne, Austin McDannald, Brian DeCost

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.
Citation
Digital Discovery
Volume
3

Citation

Kusne, A. , McDannald, A. and DeCost, B. (2024), Learning material synthesis–process–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning, Digital Discovery, [online], https://doi.org/10.1039/D4DD00048J, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936918 (Accessed April 3, 2025)

Issues

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Created September 27, 2024, Updated February 14, 2025