Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network
Jaehong Park, Youngseon Shim, Franklin Lee, Aravind Rammohan, Sushmit Goyal, Mun‐Bo Shim, Changwook Jeong, Dae Sin Kim
Abstract
, as well as a potential transferability to predict other properties associated with a backbone rigidity. Our results indicate both the capability and limitations of the GCN in learning to describe polymer systems depending on the property.
Topics & Concepts
PolymerComputer scienceBiological systemGraphElastic net regularizationSubspace topologyDimensionality reductionArtificial intelligenceMaterials scienceConvolutional neural networkRepresentation (politics)AlgorithmTheoretical computer scienceFeature selectionPolitical sciencePoliticsLawComposite materialBiologyMachine Learning in Materials ScienceComputational Drug Discovery MethodsPolymer crystallization and properties