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• W4283463809 endingPage "5051" @default.
• W4283463809 startingPage "5037" @default.
• W4283463809 abstract "Self-assembly of dilute sequence-defined macromolecules is a complex phenomenon in which the local arrangement of chemical moieties can lead to the formation of long-range structure. The dependence of this structure on the sequence necessarily implies that a mapping between the two exists, yet it has been difficult to model so far. Predicting the aggregation behavior of these macromolecules is challenging due to the lack of effective order parameters, a vast design space, inherent variability, and high computational costs associated with currently available simulation techniques. Here, we accurately predict the morphology of aggregates self-assembled from sequence-defined macromolecules using supervised machine learning. We find that regression models with implicit representation learning perform significantly better than those based on engineered features such as k-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." @default.
• W4283463809 created "2022-06-26" @default.
• W4283463809 creator A5004021471 @default.
• W4283463809 creator A5015386412 @default.
• W4283463809 creator A5035545283 @default.
• W4283463809 creator A5052698852 @default.
• W4283463809 date "2022-01-01" @default.
• W4283463809 modified "2023-10-16" @default.
• W4283463809 title "Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks" @default.
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• W4283463809 cites W1845618662 @default.
• W4283463809 cites W1965991172 @default.
• W4283463809 cites W1966315732 @default.
• W4283463809 cites W1967101381 @default.
• W4283463809 cites W1978540384 @default.
• W4283463809 cites W1978543213 @default.
• W4283463809 cites W1979951417 @default.
• W4283463809 cites W1994097939 @default.
• W4283463809 cites W2000896867 @default.
• W4283463809 cites W2003194599 @default.
• W4283463809 cites W2004369921 @default.
• W4283463809 cites W2005004586 @default.
• W4283463809 cites W2006417950 @default.
• W4283463809 cites W2016452144 @default.
• W4283463809 cites W2022116094 @default.
• W4283463809 cites W2039917458 @default.
• W4283463809 cites W2048621586 @default.
• W4283463809 cites W2058924583 @default.
• W4283463809 cites W2060261315 @default.
• W4283463809 cites W2064675550 @default.
• W4283463809 cites W2066697261 @default.
• W4283463809 cites W2071233541 @default.
• W4283463809 cites W2072103471 @default.
• W4283463809 cites W2075370047 @default.
• W4283463809 cites W2078391824 @default.
• W4283463809 cites W2086471699 @default.
• W4283463809 cites W2094944189 @default.
• W4283463809 cites W2095095557 @default.
• W4283463809 cites W2100287780 @default.
• W4283463809 cites W2116372775 @default.
• W4283463809 cites W2141712887 @default.
• W4283463809 cites W2142211900 @default.
• W4283463809 cites W2154455129 @default.
• W4283463809 cites W2154957104 @default.
• W4283463809 cites W2155704077 @default.
• W4283463809 cites W2157177751 @default.
• W4283463809 cites W2160346293 @default.
• W4283463809 cites W2325790429 @default.
• W4283463809 cites W2331224786 @default.
• W4283463809 cites W2626719974 @default.
• W4283463809 cites W2752797940 @default.
• W4283463809 cites W2762501848 @default.
• W4283463809 cites W2791679819 @default.
• W4283463809 cites W2804422193 @default.
• W4283463809 cites W2804957616 @default.
• W4283463809 cites W2807691742 @default.
• W4283463809 cites W2906621936 @default.
• W4283463809 cites W2908531463 @default.
• W4283463809 cites W2912807874 @default.
• W4283463809 cites W2913660937 @default.
• W4283463809 cites W2921365922 @default.
• W4283463809 cites W2940438782 @default.
• W4283463809 cites W2944931654 @default.
• W4283463809 cites W2951725148 @default.
• W4283463809 cites W2972528761 @default.
• W4283463809 cites W2999117165 @default.
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• W4283463809 cites W3020954634 @default.
• W4283463809 cites W3023199891 @default.
• W4283463809 cites W3027337115 @default.
• W4283463809 cites W3039310657 @default.
• W4283463809 cites W3089239941 @default.
• W4283463809 cites W3091459767 @default.
• W4283463809 cites W3093821973 @default.
• W4283463809 cites W3100633106 @default.
• W4283463809 cites W3101866624 @default.
• W4283463809 cites W3111065031 @default.
• W4283463809 cites W3115675730 @default.
• W4283463809 cites W3118299338 @default.
• W4283463809 cites W3138638921 @default.
• W4283463809 cites W3139632416 @default.
• W4283463809 cites W3155106107 @default.
• W4283463809 cites W3156578609 @default.
• W4283463809 cites W3158885334 @default.
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• W4283463809 cites W3188398181 @default.
• W4283463809 cites W3202625860 @default.
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• W4283463809 cites W4210810773 @default.
• W4283463809 cites W4213243133 @default.
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• W4283463809 doi "https://doi.org/10.1039/d2sm00452f" @default.
• W4283463809 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35748651" @default.
• W4283463809 hasPublicationYear "2022" @default.
• W4283463809 type Work @default.
• W4283463809 citedByCount "14" @default.

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