Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks
Debjyoti Bhattacharya, Devon C. Kleeblatt, Antonia Statt, Wesley F. Reinhart
Abstract
-mer counting, and a recurrent-neural-network-based regressor performs the best out of nine model architectures we tested. Furthermore, we demonstrate the high-throughput screening of monomer sequences using the regression model to identify candidates for self-assembly into selected morphologies. Our strategy is shown to successfully identify multiple suitable sequences in every test we performed, so we hope the insights gained here can be extended to other increasingly complex design scenarios in the future, such as the design of sequences under polydispersity and at varying environmental conditions.
Topics & Concepts
Sequence (biology)Representation (politics)Computer scienceAggregate (composite)Artificial neural networkMacromoleculeArtificial intelligenceBiological systemSequence spaceMachine learningAlgorithmNanotechnologyChemistryMathematicsMaterials scienceBiologyLawBiochemistryBanach spacePoliticsPolitical sciencePure mathematicsAdvanced Polymer Synthesis and CharacterizationMachine Learning in Materials ScienceBlock Copolymer Self-Assembly