NIST · ИТ · b1246936c33df9120f7f
Leveraging Theory for Enhanced Machine Learning
The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the need to understand the physics behind the machine-learning prediction. The former problem is
NIST · ИТОфициальный источник · 2022-08-26
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"title_en": "Leveraging Theory for Enhanced Machine Learning",
"designation": "",
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"date": "2022-08-26",
"date_original": "August 26, 2022",
"url": "https://www.nist.gov/publications/leveraging-theory-enhanced-machine-learning",
"source": "NIST Publications",
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"authors_en": "Debra Audus , Austin McDannald , Brian DeCost",
"abstract_excerpt_en": "The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the need to understand the physics behind the machine-learning prediction. The former problem is",
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"title_en": "Leveraging Theory for Enhanced Machine Learning",
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"authors_en": "Debra Audus , Austin McDannald , Brian DeCost",
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