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Prediction of Glass Transition Temperatures from Monomer and Repeat Unit Structure Using Computational Neural Networks
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Outgoing claims: the papers this work cites, builds on, or argues with.
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inferred← citeshttps://doi.org/10.2751/jcac.20.84openalex-snapshot
[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Relationships of Polymer Properties and Predicting Polymer Properties Based on Monomer Structure Information
inferred← citeshttps://doi.org/10.3139/113.110242openalex-snapshot
Predicting Critical Micelle Concentration Values of Non-Ionic Surfactants by Using Artificial Neural Networks
inferred← citeshttps://doi.org/10.3139/146.110640openalex-snapshot
Prediction of glass transition temperatures of aromatic heterocyclic polymers
inferred← citeshttps://doi.org/10.3389/fchem.2019.00375openalex-snapshot
Neural Networks Are Promising Tools for the Prediction of the Viscosity of Unsaturated Polyester Resins
inferred← citeshttps://doi.org/10.3390/app142210413openalex-snapshot
Understanding Polymers Through Transfer Learning and Explainable AI
inferred← citeshttps://doi.org/10.3390/polym13111898openalex-snapshot
Predicting Polymers’ Glass Transition Temperature by a Chemical Language Processing Model
inferred← citeshttps://doi.org/10.3390/polym18141727openalex-snapshot
Glass Transition Prediction of Binary Copolymers Across Large Chemical Spaces Using Machine Learning and Physics-Based Modeling
inferred← citeshttps://doi.org/10.3390/polym18192445openalex-snapshot
What Random Splits Measure in Polymer Glass-Transition Prediction: Chemistry Holdouts, Applicability Domains, and Prior Integration
inferred← citeshttps://doi.org/10.4028/scientific5/amr.455-456.436openalex-snapshot
Prediction of Glass Transition Temperature of Polymer by Support Vector Regression
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