<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Giro, Ronaldo</dc:creator> <dc:creator>Hsu, Hsianghan</dc:creator> <dc:creator>Kishimoto, Akihiro</dc:creator> <dc:creator>Neumann, Rodrigo F.</dc:creator> <dc:creator>Takeda, Seiji</dc:creator> <dc:creator>Hamada, Lisa</dc:creator> <dc:creator>B. Steiner, Mathias</dc:creator> <dc:date>2022-05-10</dc:date> <dc:description>Data sets and scripts for computational discovery of polymer membranes for carbon dioxide separation. The training data set with 1,169 homo-polymers provides carbon dioxide permeability, glass transition temperature and half decomposition temperature for each listed material. The output data set contains 784 optimized homo-polymer candidates generated by Inverse Design and Machine Learning techniques. The Jupyter notebook enables the use of the Polymer Property Prediction Engine as a service for generating the properties provided in the training data set.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2022.61</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:p8-ey</dc:identifier> <dc:identifier>mcid:2022.61</dc:identifier> <dc:identifier>oai:materialscloud.org:1337</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Materials Cloud non-exclusive license to distribute v1.0 https://www.materialscloud.org/licenses/nonexclusive-distrib/1.0</dc:rights> <dc:subject>Polymeric membranes</dc:subject> <dc:subject>CO2 separation</dc:subject> <dc:subject>Carbon dioxide separation</dc:subject> <dc:title>Automatized discovery of polymer membranes with AI generative design and molecular dynamics simulations</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>