De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks

Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/31373

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DC Field	Value	Language
dc.contributor.author	Buin A	-
dc.contributor.author	Chiang HY	-
dc.contributor.author	Gadsden SA	-
dc.contributor.author	Alderson FA	-
dc.date.accessioned	2025-03-03T23:39:38Z	-
dc.date.available	2025-03-03T23:39:38Z	-
dc.date.issued	2023-10-26	-
dc.identifier.uri	http://hdl.handle.net/11375/31373	-
dc.subject	46 Information and Computing Sciences	-
dc.subject	4611 Machine Learning	-
dc.subject	Networking and Information Technology R&D (NITRD)	-
dc.subject	Bioengineering	-
dc.subject	Neurosciences	-
dc.subject	Machine Learning and Artificial Intelligence	-
dc.subject	1.1 Normal biological development and functioning	-
dc.title	De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks	-
dc.type	Article	-
dc.date.updated	2025-03-03T23:39:37Z	-
dc.contributor.department	Mechanical Engineering	-
dc.identifier.doi	https://doi.org/10.48550/arxiv.2310.17341	-
Appears in Collections:	Mechanical Engineering Publications

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