b16bf39fb623a9c35f5c · Learning material synthesis-process-structure-property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning
Learning material synthesis-process-structure-property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning
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
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"title_en": "Learning material synthesis-process-structure-property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning",
"designation": "",
"series": "",
"status": "",
"date": "2024-09-27",
"date_original": "September 27, 2024",
"url": "https://www.nist.gov/publications/learning-material-synthesis-process-structure-property-relationship-data-fusion",
"source": "NIST Publications",
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"authors_en": "A. Gilad Kusne , Austin McDannald, Brian DeCost",
"abstract_excerpt_en": "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",
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{
"title_en": "Learning material synthesis-process-structure-property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning",
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"date": "2024-09-27",
"date_original": "September 27, 2024",
"url": "https://www.nist.gov/publications/learning-material-synthesis-process-structure-property-relationship-data-fusion",
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"authors_en": "A. Gilad Kusne , Austin McDannald, Brian DeCost",
"abstract_excerpt_en": "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"
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