http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022044769-A1

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classificationCPCAdditional http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-70
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http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C60-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
filingDate 2020-04-16-04:00^^<http://www.w3.org/2001/XMLSchema#date>
inventor http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a3554fbd8fff7a58115aff8ed07025ce
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_791ea8609802ceb6e9433d377b5e0117
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7a629dc9465d82334fc39a1d1b0cba7f
publicationDate 2022-02-10-04:00^^<http://www.w3.org/2001/XMLSchema#date>
publicationNumber US-2022044769-A1
titleOfInvention Systems and Methods for Prediction of Polymer Properties
abstract Disclosed herein is a polymer prediction system, comprising a deep learning neural network and a training dataset. The deep learning neural network can comprise: a property branch comprising two or more layers, each layer having a plurality of neurons; a polymer branch comprising two or more layers, each layer having a plurality neurons; and a merged layer including a concatenation operation, the concatenation operation configured to concatenate the property branch and the polymer branch. The training dataset can include a plurality of known polymers and a plurality of descriptors for each of the plurality of known polymers. Also disclosed herein are methods of using the same.
isCitedBy http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2023156543-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115713987-A
priorityDate 2019-04-19-04:00^^<http://www.w3.org/2001/XMLSchema#date>
type http://data.epo.org/linked-data/def/patent/Publication

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Total number of triples: 26.