NIST · ИТ · f95b4441375c3cf06626
Applying graph neural network models to molecular property prediction using high-quality experimental data
Graph neural networks have been successfully applied to machine learning models related to molecules and crystals, due to the similarity between a molecule/crystal and a graph. In this paper, we present three models that are trained with high-quality
NIST · ИТОфициальный источник · 2024-06-01
{
"title_en": "Applying graph neural network models to molecular property prediction using high-quality experimental data",
"designation": "",
"series": "",
"status": "",
"date": "2024-06-01",
"date_original": "June 1, 2024",
"url": "https://www.nist.gov/publications/applying-graph-neural-network-models-molecular-property-prediction-using-high-quality",
"source": "NIST Publications",
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"authors_en": "Barry I. Schneider , Anthony J. Kearsley , Walid Keyrouz , Thomas C. Allison , chen qu",
"abstract_excerpt_en": "Graph neural networks have been successfully applied to machine learning models related to molecules and crystals, due to the similarity between a molecule/crystal and a graph. In this paper, we present three models that are trained with high-quality",
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{
"title_en": "Applying graph neural network models to molecular property prediction using high-quality experimental data",
"designation": "",
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"date": "2024-06-01",
"date_original": "June 1, 2024",
"url": "https://www.nist.gov/publications/applying-graph-neural-network-models-molecular-property-prediction-using-high-quality",
"source": "NIST Publications",
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"authors_en": "Barry I. Schneider , Anthony J. Kearsley , Walid Keyrouz , Thomas C. Allison , chen qu",
"abstract_excerpt_en": "Graph neural networks have been successfully applied to machine learning models related to molecules and crystals, due to the similarity between a molecule/crystal and a graph. In this paper, we present three models that are trained with high-quality"
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