Hostname: page-component-745bb68f8f-lrblm Total loading time: 0 Render date: 2025-01-13T14:31:28.372Z Has data issue: false hasContentIssue false
  • English
  • Français

Challenges and opportunities of polymer design with machine learning and high throughput experimentation

Published online by Cambridge University Press:  03 May 2019

Jatin N. Kumar Open the ORCID record for Jatin N. Kumar [Opens in a new window] ,
Qianxiao Li  and
Ye Jun
Jatin N. Kumar*
Affiliation:
Institute of Materials Research & Engineering, 2 Fusionopolis Way, #08-03, 138634, Singapore
Qianxiao Li
Affiliation:
Institute of High-Performance Computing, 1 Fusionopolis Way, #16-16, 138632, Singapore
Ye Jun
Affiliation:
Institute of High-Performance Computing, 1 Fusionopolis Way, #16-16, 138632, Singapore
*
*Address all correspondence to Jatin N. Kumar at jatinkumar@mac.com and kumarjn@imre.a-star.edu.sg
Get access

Abstract

In this perspective, the authors challenge the status quo of polymer innovation. The authors first explore how research in polymer design is conducted today, which is both time consuming and unable to capture the multi-scale complexities of polymers. The authors discuss strategies that could be employed in bringing together machine learning, data curation, high-throughput experimentation, and simulations, to build a system that can accurately predict polymer properties from their descriptors and enable inverse design that is capable of designing polymers based on desired properties.

Type
Artificial Intelligence Prospectives
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 537 - 544
Copyright
Copyright © Materials Research Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Gregory, A. and Stenzel, M.H.: Complex polymer architectures via RAFT polymerization: From fundamental process to extending the scope using click chemistry and nature's building blocks. Prog. Polym. Sci. 37, 38 (2012).Google Scholar
2.Garcia, S.J.: Effect of polymer architecture on the intrinsic self-healing character of polymers. Eur. Polym. J. 53, 118 (2014).Google Scholar
3.Rinkenauer, A.C., Schubert, S., Traeger, A. and Schubert, U.S.: The influence of polymer architecture on in vitro pDNA transfection. J. Mater. Chem. B 3, 7477 (2015).Google Scholar
4.Dag, A., Callari, M., Lu, H. and Stenzel, M.H.: Modulating the cellular uptake of platinum drugs with glycopolymers. Polymer Chemistry 7, 1031 (2016).Google Scholar
5.Paramelle, D., Gorelik, S., Liu, Y. and Kumar, J.: Photothermally responsive gold nanoparticle conjugated polymer-grafted porous hollow silica nanocapsules. Chem. Commun. 52, 9897 (2016).Google Scholar
6.Kumar, J., Bousquet, A. and Stenzel, M.H.: Thiol-alkyne Chemistry for the Preparation of Micelles with Glycopolymer Corona: Dendritic Surfaces versus Linear Glycopolymer in Their Ability to Bind to Lectins. Macromol. Rapid Commun. 32, 1620 (2011).Google Scholar
7.Kumar, J., McDowall, L., Chen, G. and Stenzel, M.H.: Synthesis of thermo-responsive glycopolymersviacopper catalysed azide-alkyne ‘click’ chemistry for inhibition of ricin: the effect of spacer between polymer backbone and galactose. Polymer Chemistry 2, 1879 (2011).Google Scholar
8.Correa-Baena, J.-P., Hippalgaonkar, K., van Duren, J., Jaffer, S., Chandrasekhar, V.R., Stevanovic, V., Wadia, C., Guha, S. and Buonassisi, T.: Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing. Joule 2, 1410 (2018).Google Scholar
9.Bicerano, J.: Prediction of Polymer Properties, (Taylor & Francis Inc, Bosa Roca, United States, 2002).Google Scholar
10.Stuart, M.A.C., Huck, W.T.S., Genzer, J., Muller, M., Ober, C., Stamm, M., Sukhorukov, G.B., Szleifer, I., Tsukruk, V.V., Urban, M., Winnik, F., Zauscher, S., Luzinov, I. and Minko, S.: Emerging applications of stimuli-responsive polymer materials. Nat. Mater. 9, 101 (2010).Google Scholar
11.Jiang, R., Jin, Q., Li, B., Ding, D. and Shi, A.-C.: Phase Diagram of Poly(ethylene oxide) and Poly(propylene oxide) Triblock Copolymers in Aqueous Solutions. Macromolecules 39, 5891 (2006).Google Scholar
12.Ashbaugh, H.S. and Paulaitis, M.E.: Monomer Hydrophobicity as a Mechanism for the LCST Behavior of Poly(ethylene oxide) in Water. Ind. Eng. Chem. Res. 45, 5531 (2006).Google Scholar
13.Halperin, A., Kröger, M. and Winnik, F.M.: Poly(N-isopropylacrylamide) Phase Diagrams: Fifty Years of Research. Angew. Chem. Int. Ed. 54, 15342 (2015).Google Scholar
14.Hoogenboom, R., Thijs, H.M.L., Jochems, M.J.H.C., van Lankvelt, B.M., Fijten, M.W.M. and Schubert, U.S.: Tuning the LCST of poly(2-oxazoline)s by varying composition and molecular weight: alternatives to poly(N-isopropylacrylamide)? Chem. Commun. 0, 5758 (2008).Google Scholar
15.Odian, G.: Principles of Polymerization, Fourth Edition ed. (John Wiley & Sons, New York, United States, 2004).Google Scholar
16.Smith, J.S., Isayev, O. and Roitberg, A.E.: ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci 8, 3192 (2017).Google Scholar
17.Anderson, T.W.: An Introduction To Multivariate Statistical Analysis, (Wiley, New York, 1958).Google Scholar
18.Box, G.E.P. and Tiao, G.C.: Bayesian Inference in Statistical Analysis, (John Wiley & Sons, New York, United States, 2011).Google Scholar
19.Cortes, C. and Vapnik, V.: Support-Vector Networks. Machin. Learn. 20, 273 (1995).Google Scholar
20.Rokach, L. and Maimon, O.: Data Mining With Decision Trees: Theory and Applications, (World Scientific Publishing Co., Inc.2014).Google Scholar
21.LeCun, Y., Bengio, Y. and Hinton, G.: Deep learning. Nature 521, 436 (2015).Google Scholar
22.Friedman, J.H.: Greedy Function Approximation: A Gradient Boosting Machine. Ann. Stat. 29, 1189 (2001).Google Scholar
23.Aseyev, V., Tenhu, H. and Winnik, F.M.: Non-ionic Thermoresponsive Polymers in Water, in Self Organized Nanostructures of Amphiphilic Block Copolymers II, edited by Müller, A. H. E. and Borisov, O. (Springer Berlin Heidelberg, Berlin, Heidelberg, 2011), pp. 29.Google Scholar
24.Wei, J.N., Duvenaud, D. and Aspuru-Guzik, A.: Neural Networks for the Prediction of Organic Chemistry Reactions. ACS Cent. Sci 2, 725 (2016).Google Scholar
25.Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., Hirzel, T.D., Duvenaud, D., Maclaurin, D., Blood-Forsythe, M.A., Chae, H.S., Einzinger, M., Ha, D.-G., Wu, T., Markopoulos, G., Jeon, S., Kang, H., Miyazaki, H., Numata, M., Kim, S., Huang, W., Hong, S.I., Baldo, M., Adams, R.P. and Aspuru-Guzik, A.: Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15, 1120 (2016).Google Scholar
26.Häse, F., Kreisbeck, C. and Aspuru-Guzik, A.: Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. Chem. Sci 8, 8419 (2017).Google Scholar
27.Benjamin, S.-L., Carlos, O., Gabriel L., G. and Alan, A.-G.: Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC), (ChemRxiv, 2017), p. 10.26434/chemrxiv.5309668.v3.Google Scholar
28.Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gomez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A. and Adams, R.P.: Convolutional networks on graphs for learning molecular fingerprints, in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (MIT Press, Montreal, Canada, 2015), pp. 2224.Google Scholar
29.Gómez-Bombarelli, R., Wei, J.N., Duvenaud, D., Hernández-Lobato, J.M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T.D., Adams, R.P. and Aspuru-Guzik, A.: Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Cent. Sci 4, 268 (2018).Google Scholar
30.Huan, T.D., Mannodi-Kanakkithodi, A., Kim, C., Sharma, V., Pilania, G. and Ramprasad, R.: A polymer dataset for accelerated property prediction and design. Sci. Data 3, 160012 (2016).Google Scholar
31.Mannodi-Kanakkithodi, A., Pilania, G., Huan, T.D., Lookman, T. and Ramprasad, R.: Machine Learning Strategy for Accelerated Design of Polymer Dielectrics. Sci. Rep. 6, 20952 (2016).Google Scholar
32.Kim, C., Chandrasekaran, A., Huan, T.D., Das, D. and Ramprasad, R.: Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions. J. Phys. Chem. C 122, 17575 (2018).Google Scholar
33.Zeng, M., Kumar, J.N., Zeng, Z., Ramasamy, S., Chandrasekhar, V.R. and Hippalgaonkar, K.: Graph Convolutional Neural Networks for Polymers Property Prediction. arXiv, 1811.06231 (2018).Google Scholar
34.Wei, Q., Melko, R.G. and Chen, J.Z.Y.: Identifying polymer states by machine learning. Physical Review E 95, 032504 (2017).Google Scholar
35.Kumar, J., Li, Q., Tang, K.Y.T., Buonassisi, T., Gonzalez-Oyarce, A.L. and Ye, J.: Machine Learning Enables Polymer Cloud-Point Engineering via Inverse Design, (ChemRxiv, 2018), p. 10.26434/chemrxiv.7528343.v1.Google Scholar
36.Luca, M.G., Jan, V., Emre, A., Runhai, O., Sergey, V.L., Claudia, D. and S. Matthias: Learning physical descriptors for materials science by compressed sensing. New Journal of Physics 19, 023017 (2017).Google Scholar
37.Dünweg, B. and Kremer, K.: Molecular dynamics simulation of a polymer chain in solution. The Journal of Chemical Physics 99, 6983 (1993).Google Scholar
38.Groot, R.D. and Warren, P.B.: Dissipative particle dynamics: Bridging the gap between atomistic and mesoscopic simulation. The Journal of Chemical Physics 107, 4423 (1997).Google Scholar
39.Kutzner, C., Páll, S., Fechner, M., Esztermann, A., de Groot, B.L. and Grubmüller, H.: Best bang for your buck: GPU nodes for GROMACS biomolecular simulations. Journal of Computational Chemistry 36, 1990 (2015).Google Scholar
40.Oliver, S., Zhao, L., Gormley, A.J., Chapman, R. and Boyer, C.: Living in the Fast Lane—High Throughput Controlled/Living Radical Polymerization. Macromolecules 52, 3 (2018).Google Scholar
41.Nikolaev, P., Hooper, D., Webber, F., Rao, R., Decker, K., Krein, M., Poleski, J., Barto, R. and Maruyama, B.: Autonomy in materials research: a case study in carbon nanotube growth. Npj Computational Materials 2, 16031 (2016).Google Scholar