Graph convolutional neural networks as "general-purpose" property predictors: the universality and l...
Graph convolutional neural networks as "general-purpose" property predictors: the universality and limits of applicability
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Contents
Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. Classical approach of rigorous feature engineering in ML typically improves the performance of the predictive model, but at...
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Full title
Graph convolutional neural networks as "general-purpose" property predictors: the universality and limits of applicability
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TN_cdi_proquest_journals_2241819170
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2241819170
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E-ISSN
2331-8422
DOI
10.48550/arxiv.1906.06256
