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Machine learning in combinatorial polymer chemistry

The design of new functional polymers depends on the successful navigation of their structure-function landscapes. Advances in combinatorial polymer chemistry and machine learning provide exciting opportunities for the engineering of fit-for-purpose polymeric materials.

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Fig. 1: Robotic systems for autonomous structure-function testing in combinatorial polymer libraries.

References

  1. 1.

    Hoogenboom, R., Meier, M. A. R. & Schubert, U. S. Combinatorial methods, automated synthesis and high-throughput screening in polymer research: past and present. Macromol. Rapid Comm. 24, 15–32 (2003).

    CAS  Article  Google Scholar 

  2. 2.

    Anderson, D. G., Lynn, D. M. & Langer, R. Semi-automated synthesis and screening of a large library of degradable cationic polymers for gene delivery. Angew. Chem. Int. Ed. 42, 3153–3158 (2003).

    CAS  Article  Google Scholar 

  3. 3.

    Yeow, J., Chapman, R., Gormley, A. J. & Boyer, C. Up in the air: oxygen tolerance in controlled/living radical polymerization. Chem. Soc. Rev. 47, 4357–4387 (2018).

    CAS  Article  Google Scholar 

  4. 4.

    Tamasi, M., Kosuri, S., DiStefano, J., Chapman, R. & Gormley, A. J. Automation of controlled/living radical polymerization. Adv. Intell. Syst. 2, 1900126 (2020).

    Article  Google Scholar 

  5. 5.

    Jain, A. et al. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).

    Article  Google Scholar 

  6. 6.

    Webb, M. A., Jackson, N. E., Gil, P. S. & de Pablo, J. J. Targeted sequence design within the coarse-grained polymer genome. Sci. Adv. 6, eabc6216 (2020).

    Article  Google Scholar 

  7. 7.

    Shmilovich, K. et al. Discovery of self-assembling π-conjugated peptides by active learning-directed coarse-grained molecular simulation. J. Phys. Chem. B 124, 3873–3891 (2020).

    CAS  Article  Google Scholar 

  8. 8.

    Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15, 1120–1127 (2016).

    Article  Google Scholar 

  9. 9.

    Chen, L. et al. Frequency-dependent dielectric constant prediction of polymers using machine learning. npj Comput. Mater. 6, 61 (2020).

    CAS  Article  Google Scholar 

  10. 10.

    Lin, T. S. et al. BigSMILES: a structurally-based line notation for describing macromolecules. ACS Cent. Sci. 5, 1523–1531 (2019).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

A.J.G. was supported by NSF CBET award number 2009942 and NIH NIGMS award number 1R35GM138296-01. M.A.W. acknowledges support from Princeton University.

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Both authors contributed equally to the preparation of the manuscript.

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Correspondence to Adam J. Gormley or Michael A. Webb.

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Related links

CHEMnetBASE: http://poly.chemnetbase.com/

Critical Assessment of Protein Structure Prediction Competition: https://predictioncenter.org

Materials data facility: https://materialsdatafacility.org/

Materials Genome Initiative: https://www.mgi.gov/

PoLyInfo: https://polymer.nims.go.jp/en/

Polymer Genome: https://www.polymergenome.org/

Polymer Property Predictor and Database: https://pppdb.uchicago.edu/

Protein Data Bank: http://www.wwpdb.org/

QM9: http://quantum-machine.org/datasets/

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Gormley, A.J., Webb, M.A. Machine learning in combinatorial polymer chemistry. Nat Rev Mater 6, 642–644 (2021). https://doi.org/10.1038/s41578-021-00282-3

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