Polymer design using genetic algorithm and machine learning
[Display omitted] •Genetic algorithm achieves new polymer designs with high bandgap and high glass transition temperature.•Machine learning prediction models assist rapid evaluation of fitness function.•Chemical fragments leading high performance of polymers are highlighted. Data driven or machine l...
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| Published in | Computational materials science Vol. 186; p. 110067 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.01.2021
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| Subjects | |
| Online Access | Get full text |
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| Abstract | [Display omitted]
•Genetic algorithm achieves new polymer designs with high bandgap and high glass transition temperature.•Machine learning prediction models assist rapid evaluation of fitness function.•Chemical fragments leading high performance of polymers are highlighted.
Data driven or machine learning (ML) based methods have been recently used in materials science to provide quick material property predictions. Although powerful and robust, these predictive models are still limited in terms of their applicability towards the design of materials with target property or performance objectives. Here, we employ a nature-mimicking optimization method, the genetic algorithm, in tandem with ML-based predictive models to design polymers that meet practically useful, but extreme, property criteria (i.e., glass transition temperature, Tg>500 K and bandgap, Eg>6 eV). Analogous to nature, the characteristic properties of a polymer are assumed to be determined by the constituting types and sequence of chemical building blocks (or fragments) in the monomer unit. Evolution of polymers by natural operations of crossover, mutation, and selection over 100 generations leads to creation of 132 new (as compared to 4 already known cases) and chemically unique polymers with high Tg and Eg. Chemical guidelines on what fragments make up polymers with extreme thermal and electrical performance metrics have been selected and revealed by the algorithm. The approach presented here is general and can be extended to design polymers with different property objectives. |
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| AbstractList | [Display omitted]
•Genetic algorithm achieves new polymer designs with high bandgap and high glass transition temperature.•Machine learning prediction models assist rapid evaluation of fitness function.•Chemical fragments leading high performance of polymers are highlighted.
Data driven or machine learning (ML) based methods have been recently used in materials science to provide quick material property predictions. Although powerful and robust, these predictive models are still limited in terms of their applicability towards the design of materials with target property or performance objectives. Here, we employ a nature-mimicking optimization method, the genetic algorithm, in tandem with ML-based predictive models to design polymers that meet practically useful, but extreme, property criteria (i.e., glass transition temperature, Tg>500 K and bandgap, Eg>6 eV). Analogous to nature, the characteristic properties of a polymer are assumed to be determined by the constituting types and sequence of chemical building blocks (or fragments) in the monomer unit. Evolution of polymers by natural operations of crossover, mutation, and selection over 100 generations leads to creation of 132 new (as compared to 4 already known cases) and chemically unique polymers with high Tg and Eg. Chemical guidelines on what fragments make up polymers with extreme thermal and electrical performance metrics have been selected and revealed by the algorithm. The approach presented here is general and can be extended to design polymers with different property objectives. |
| ArticleNumber | 110067 |
| Author | Chen, Lihua Kim, Chiho Batra, Rohit Tran, Huan Ramprasad, Rampi |
| Author_xml | – sequence: 1 givenname: Chiho orcidid: 0000-0002-1814-4980 surname: Kim fullname: Kim, Chiho – sequence: 2 givenname: Rohit orcidid: 0000-0002-1098-7035 surname: Batra fullname: Batra, Rohit – sequence: 3 givenname: Lihua orcidid: 0000-0002-9852-8211 surname: Chen fullname: Chen, Lihua – sequence: 4 givenname: Huan orcidid: 0000-0002-8093-9426 surname: Tran fullname: Tran, Huan – sequence: 5 givenname: Rampi surname: Ramprasad fullname: Ramprasad, Rampi email: rampi.ramprasad@mse.gatech.edu |
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| Cites_doi | 10.1103/PhysRevB.92.014106 10.1038/srep20952 10.1038/sdata.2016.12 10.1515/polyeng-2019-0329 10.1088/1361-651X/aaf8ca 10.1063/5.0008026 10.1007/s10853-015-9369-2 10.1557/mrc.2019.78 10.1002/adma.201600377 10.1002/cmdc.200800178 10.1021/acs.jcim.9b00656 10.1038/s41524-020-0333-6 10.1016/j.polymer.2018.10.017 10.1016/j.pmatsci.2016.05.001 10.1021/acscentsci.7b00572 10.1016/j.mattod.2017.11.021 10.1109/EIDWT.2011.13 10.1021/acs.chemmater.7b02027 10.1021/acs.jpcc.8b02913 |
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| References | Huan, Boggs, Teyssedre, Laurent, Cakmak, Kumar, Ramprasad (b0025) 2016; 83 H. Dai, Y. Tian, B. Dai, S. Skiena, L. Song, Syntax-directed variational autoencoder for structured data, arXiv:1802.08786. URL: https://arxiv.org/abs/1802.08786. RDKit, open source toolkit for cheminformatics. URL Mannodi-Kanakkithodi, Pilania, Huan, Lookman, Ramprasad (b0070) 2016; 6 S. Venkatram, C. Kim, A. Chandrasekaran, R. Ramprasad, Critical assessment of the hildebrand and hansen solubility parameters for polymers, J. Chem. Inf. Model. 59 (10) (2019) 4188–4194, pMID: 31545900. arXiv:https://doi.org/10.1021/acs.jcim.9b00656, doi:10.1021/acs.jcim.9b00656. URL: doi: 10.1021/acs.jcim.9b00656. Mannodi-Kanakkithodi, Huan, Ramprasad (b0055) 2017; 29 Chen, Huan, Quintero, Ramprasad (b0045) 2016; 51 Lightstone, Chen, Kim, Batra, Ramprasad (b0035) 2020; 127 Q. Liu, M. Allamanis, M. Brockschmidt, A. Gaunt, Constrained graph variational autoencoders for molecule design, in: S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (Eds.), Advances in Neural Information Processing Systems 31, Curran Associates Inc, 2018, pp. 7795–7804. URL: http://papers.nips.cc/paper/8005-constrained-graph-variational-autoencoders-for-molecule-design.pdf. Mannodi-Kanakkithodi, Treich, Huan, Ma, Tefferi, Cao, Sotzing, Ramprasad (b0020) 2016; 28 C. Kim, A. Chandrasekaran, T.D. Huan, D. Das, R. Ramprasad, Polymer genome: A data-powered polymer informatics platform for property predictions, J. Phys. Chem. C 122 (31) (2018) 17575–17585. arXiv:https://doi.org/10.1021/acs.jpcc.8b02913, doi:10.1021/acs.jpcc.8b02913. URL: doi: 10.1021/acs.jpcc.8b02913. van Krevelen (b0005) 1997 C. Wu, A.A. Deshmukh, Z. Li, L. Chen, A. Alamri, Y. Wang, R. Ramprasad, G.A. Sotzing, Y. Cao, Flexible temperature-invariant polymer dielectrics with large bandgap, Adv. Mater. 2000499 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.202000499, doi:10.1002/adma.202000499. URL Huan, Mannodi-Kanakkithodi, Ramprasad (b0075) 2015; 92 Kim, Chandrasekaran, Jha, Ramprasad (b0050) 2019; 9 Chen, Kim, Batra, Lightstone, Wu, Li, Deshmukh, Wang, Tran, Vashishta, Sotzing, Cao, Ramprasad (b0030) 2020; 6 M.J. Kusner, B. Paige, J.M. Hernández-Lobato, Grammar variational autoencoder, in: D. Precup, Y.W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, Vol. 70 of Proceedings of Machine Learning Research, PMLR, International Convention Centre, Sydney, Australia, 2017, pp. 1945–1954. URL J. Degen, C. Wegscheid-Gerlach, A. Zaliani, M. Rarey, On the art of compiling and using ‘drug-like’ chemical fragment spaces, ChemMedChem 3 (10) (2008) 1503–1507. arXiv:https://chemistry-europe.onlinelibrary.wiley.com/doi/pdf/10.1002/cmdc.200800178, doi:10.1002/cmdc.200800178. URL Holland (b0145) 1975 R. Gómez-Bombarelli, J.N. Wei, D. Duvenaud, J.M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T.D. Hirzel, R.P. Adams, A. Aspuru-Guzik, Automatic chemical design using a data-driven continuous representation of molecules, ACS Cent. Sci. 4 (2) (2018) 268–276, pMID: 29532027. arXiv:https://doi.org/10.1021/acscentsci.7b00572, doi:10.1021/acscentsci.7b00572. URL: doi: 10.1021/acscentsci.7b00572. S. Otsuka, I. Kuwajima, J. Hosoya, Y. Xu, M. Yamazaki, Polyinfo: Polymer database for polymeric materials design, in: 2011 International Conference on Emerging Intelligent Data and Web Technologies (EIDWT), IEEE, Tirana, 2011, pp. 22–29. doi:10.1109/EIDWT.2011.13. . Jha, Chandrasekaran, Kim, Ramprasad (b0150) 2019; 27 (b0155) 2006 J. You, B. Liu, R. Ying, V. Pande, J. Leskovec, Graph convolutional policy network for goal-directed molecular graph generation, in: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, Curran Associates Inc., Red Hook, NY, USA, 2018, p. 6412–6422. Nasreen, Treich, Baczkowski, Mannodi-Kanakkithodi, Baldwin, Scheirey, Cao, Ramprasad, Sotzing (b0065) 2018; 159 N. De Cao, T. Kipf, Molgan: An implicit generative model for small molecular graphs, arxiv.org/abs/1805.11973. URL: https://arxiv.org/abs/1805.11973. Huan, Mannodi-Kanakkithodi, Kim, Sharma, Pilania, Ramprasad (b0125) 2016; 3 Bicerano (b0130) 2002 Mannodi-Kanakkithodi, Chandrasekaran, Kim, Huan, Pilania, Botu, Ramprasad (b0010) 2018; 21 G. Lima Guimaraes, B. Sanchez-Lengeling, C. Outeiral, P. Luis Cunha Farias, A. Aspuru-Guzik, Objective-reinforced generative adversarial networks (organ) for sequence generation models, arXiv:1705.10843. URL: https://arxiv.org/abs/1705.10843. Zhu, Kim, Chandrasekarn, Everett, Ramprasad, Lively (b0040) 2020; 40 W. Jin, R. Barzilay, T. Jaakkola, Syntax-directed variational autoencoder for structured data, arXiv:1802.04364. URL: https://arxiv.org/abs/1802.04364. (b0135) 2009 10.1016/j.commatsci.2020.110067_b0105 Huan (10.1016/j.commatsci.2020.110067_b0125) 2016; 3 10.1016/j.commatsci.2020.110067_b0100 Bicerano (10.1016/j.commatsci.2020.110067_b0130) 2002 10.1016/j.commatsci.2020.110067_b0165 Chen (10.1016/j.commatsci.2020.110067_b0030) 2020; 6 10.1016/j.commatsci.2020.110067_b0120 Chen (10.1016/j.commatsci.2020.110067_b0045) 2016; 51 Mannodi-Kanakkithodi (10.1016/j.commatsci.2020.110067_b0010) 2018; 21 Lightstone (10.1016/j.commatsci.2020.110067_b0035) 2020; 127 10.1016/j.commatsci.2020.110067_b0090 10.1016/j.commatsci.2020.110067_b0095 Zhu (10.1016/j.commatsci.2020.110067_b0040) 2020; 40 Huan (10.1016/j.commatsci.2020.110067_b0025) 2016; 83 Mannodi-Kanakkithodi (10.1016/j.commatsci.2020.110067_b0070) 2016; 6 (10.1016/j.commatsci.2020.110067_b0155) 2006 van Krevelen (10.1016/j.commatsci.2020.110067_b0005) 1997 Mannodi-Kanakkithodi (10.1016/j.commatsci.2020.110067_b0055) 2017; 29 Mannodi-Kanakkithodi (10.1016/j.commatsci.2020.110067_b0020) 2016; 28 10.1016/j.commatsci.2020.110067_b0110 Jha (10.1016/j.commatsci.2020.110067_b0150) 2019; 27 (10.1016/j.commatsci.2020.110067_b0135) 2009 10.1016/j.commatsci.2020.110067_b0115 10.1016/j.commatsci.2020.110067_b0015 10.1016/j.commatsci.2020.110067_b0080 Huan (10.1016/j.commatsci.2020.110067_b0075) 2015; 92 10.1016/j.commatsci.2020.110067_b0085 10.1016/j.commatsci.2020.110067_b0140 10.1016/j.commatsci.2020.110067_b0160 10.1016/j.commatsci.2020.110067_b0060 Holland (10.1016/j.commatsci.2020.110067_b0145) 1975 Kim (10.1016/j.commatsci.2020.110067_b0050) 2019; 9 Nasreen (10.1016/j.commatsci.2020.110067_b0065) 2018; 159 |
| References_xml | – volume: 127 year: 2020 ident: b0035 article-title: Refractive index prediction models for polymers using machine learning publication-title: J. Appl. Phys. – reference: G. Lima Guimaraes, B. Sanchez-Lengeling, C. Outeiral, P. Luis Cunha Farias, A. Aspuru-Guzik, Objective-reinforced generative adversarial networks (organ) for sequence generation models, arXiv:1705.10843. URL: https://arxiv.org/abs/1705.10843. – volume: 83 start-page: 236 year: 2016 ident: b0025 article-title: Advanced polymeric dielectrics for high energy density applications publication-title: Prog. Mater. Sci. – reference: C. Wu, A.A. Deshmukh, Z. Li, L. Chen, A. Alamri, Y. Wang, R. Ramprasad, G.A. Sotzing, Y. Cao, Flexible temperature-invariant polymer dielectrics with large bandgap, Adv. Mater. 2000499 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.202000499, doi:10.1002/adma.202000499. URL: – year: 2009 ident: b0135 publication-title: Polymer Data Handbook – volume: 21 start-page: 785 year: 2018 end-page: 796 ident: b0010 article-title: Scoping the polymer genome: a roadmap for rational polymer dielectrics design and beyond publication-title: Mater. Today – reference: C. Kim, A. Chandrasekaran, T.D. Huan, D. Das, R. Ramprasad, Polymer genome: A data-powered polymer informatics platform for property predictions, J. Phys. Chem. C 122 (31) (2018) 17575–17585. arXiv:https://doi.org/10.1021/acs.jpcc.8b02913, doi:10.1021/acs.jpcc.8b02913. URL: doi: 10.1021/acs.jpcc.8b02913. – volume: 92 year: 2015 ident: b0075 article-title: Accelerated materials property predictions and design using motif-based fingerprints publication-title: Phys. Rev. B – volume: 29 start-page: 9001 year: 2017 end-page: 9010 ident: b0055 article-title: Mining materials design rules from data: the example of polymer dielectrics publication-title: Chem. Mater. – reference: RDKit, open source toolkit for cheminformatics. URL: – reference: R. Gómez-Bombarelli, J.N. Wei, D. Duvenaud, J.M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T.D. Hirzel, R.P. Adams, A. Aspuru-Guzik, Automatic chemical design using a data-driven continuous representation of molecules, ACS Cent. Sci. 4 (2) (2018) 268–276, pMID: 29532027. arXiv:https://doi.org/10.1021/acscentsci.7b00572, doi:10.1021/acscentsci.7b00572. URL: doi: 10.1021/acscentsci.7b00572. – year: 2006 ident: b0155 publication-title: Gaussian Processes for Machine Learning – reference: S. Otsuka, I. Kuwajima, J. Hosoya, Y. Xu, M. Yamazaki, Polyinfo: Polymer database for polymeric materials design, in: 2011 International Conference on Emerging Intelligent Data and Web Technologies (EIDWT), IEEE, Tirana, 2011, pp. 22–29. doi:10.1109/EIDWT.2011.13. – reference: N. De Cao, T. Kipf, Molgan: An implicit generative model for small molecular graphs, arxiv.org/abs/1805.11973. URL: https://arxiv.org/abs/1805.11973. – volume: 3 year: 2016 ident: b0125 article-title: A polymer dataset for accelerated property prediction and design publication-title: Sci. Data – volume: 159 start-page: 95 year: 2018 end-page: 105 ident: b0065 article-title: A material genome approach towards exploration of zn and cd coordination complex polyester as dielectrics: design, synthesis and characterization publication-title: Polymer – volume: 6 start-page: 20952 year: 2016 ident: b0070 article-title: Machine learning strategy for the accelerated design of polymer dielectrics publication-title: Sci. Rep. – year: 1975 ident: b0145 article-title: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence – reference: J. Degen, C. Wegscheid-Gerlach, A. Zaliani, M. Rarey, On the art of compiling and using ‘drug-like’ chemical fragment spaces, ChemMedChem 3 (10) (2008) 1503–1507. arXiv:https://chemistry-europe.onlinelibrary.wiley.com/doi/pdf/10.1002/cmdc.200800178, doi:10.1002/cmdc.200800178. URL: – volume: 28 start-page: 6277 year: 2016 end-page: 6291 ident: b0020 article-title: Rational co-design of polymer dielectrics for energy storage publication-title: Adv. Mater. – reference: M.J. Kusner, B. Paige, J.M. Hernández-Lobato, Grammar variational autoencoder, in: D. Precup, Y.W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, Vol. 70 of Proceedings of Machine Learning Research, PMLR, International Convention Centre, Sydney, Australia, 2017, pp. 1945–1954. URL: – reference: . – volume: 51 start-page: 506 year: 2016 end-page: 512 ident: b0045 article-title: Charge injection barriers at metal/polyethylene interfaces publication-title: J. Mater. Sci. – reference: J. You, B. Liu, R. Ying, V. Pande, J. Leskovec, Graph convolutional policy network for goal-directed molecular graph generation, in: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, Curran Associates Inc., Red Hook, NY, USA, 2018, p. 6412–6422. – reference: Q. Liu, M. Allamanis, M. Brockschmidt, A. Gaunt, Constrained graph variational autoencoders for molecule design, in: S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (Eds.), Advances in Neural Information Processing Systems 31, Curran Associates Inc, 2018, pp. 7795–7804. URL: http://papers.nips.cc/paper/8005-constrained-graph-variational-autoencoders-for-molecule-design.pdf. – reference: H. Dai, Y. Tian, B. Dai, S. Skiena, L. Song, Syntax-directed variational autoencoder for structured data, arXiv:1802.08786. URL: https://arxiv.org/abs/1802.08786. – volume: 27 start-page: 024002 year: 2019 ident: b0150 publication-title: Model. Simul. Mater. Sci. Eng. – reference: S. Venkatram, C. Kim, A. Chandrasekaran, R. Ramprasad, Critical assessment of the hildebrand and hansen solubility parameters for polymers, J. Chem. Inf. Model. 59 (10) (2019) 4188–4194, pMID: 31545900. arXiv:https://doi.org/10.1021/acs.jcim.9b00656, doi:10.1021/acs.jcim.9b00656. URL: doi: 10.1021/acs.jcim.9b00656. – year: 2002 ident: b0130 article-title: Prediction of Polymer Properties – volume: 9 start-page: 860 year: 2019 end-page: 866 ident: b0050 article-title: Active-learning and materials design: the example of high glass transition temperature polymers publication-title: MRS Commun. – reference: W. Jin, R. Barzilay, T. Jaakkola, Syntax-directed variational autoencoder for structured data, arXiv:1802.04364. URL: https://arxiv.org/abs/1802.04364. – year: 1997 ident: b0005 article-title: Properties of Polymers: Their Correlation with Chemical Structure – volume: 6 start-page: 61 year: 2020 ident: b0030 article-title: Frequency-dependent dielectric constant prediction of polymers using machine learning publication-title: Npj Comput. Mater. – volume: 40 start-page: 451 year: 2020 end-page: 457 ident: b0040 article-title: Polymer genome-based prediction of gas permeabilities in polymers publication-title: J. Polym. Eng. – volume: 92 year: 2015 ident: 10.1016/j.commatsci.2020.110067_b0075 article-title: Accelerated materials property predictions and design using motif-based fingerprints publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.92.014106 – volume: 6 start-page: 20952 year: 2016 ident: 10.1016/j.commatsci.2020.110067_b0070 article-title: Machine learning strategy for the accelerated design of polymer dielectrics publication-title: Sci. Rep. doi: 10.1038/srep20952 – ident: 10.1016/j.commatsci.2020.110067_b0120 – volume: 3 year: 2016 ident: 10.1016/j.commatsci.2020.110067_b0125 article-title: A polymer dataset for accelerated property prediction and design publication-title: Sci. Data doi: 10.1038/sdata.2016.12 – year: 1997 ident: 10.1016/j.commatsci.2020.110067_b0005 – volume: 40 start-page: 451 issue: 6 year: 2020 ident: 10.1016/j.commatsci.2020.110067_b0040 article-title: Polymer genome-based prediction of gas permeabilities in polymers publication-title: J. Polym. Eng. doi: 10.1515/polyeng-2019-0329 – volume: 27 start-page: 024002 issue: 2 year: 2019 ident: 10.1016/j.commatsci.2020.110067_b0150 publication-title: Model. Simul. Mater. Sci. Eng. doi: 10.1088/1361-651X/aaf8ca – ident: 10.1016/j.commatsci.2020.110067_b0160 – year: 2006 ident: 10.1016/j.commatsci.2020.110067_b0155 – volume: 127 issue: 21 year: 2020 ident: 10.1016/j.commatsci.2020.110067_b0035 article-title: Refractive index prediction models for polymers using machine learning publication-title: J. Appl. Phys. doi: 10.1063/5.0008026 – volume: 51 start-page: 506 issue: 1 year: 2016 ident: 10.1016/j.commatsci.2020.110067_b0045 article-title: Charge injection barriers at metal/polyethylene interfaces publication-title: J. Mater. Sci. doi: 10.1007/s10853-015-9369-2 – volume: 9 start-page: 860 year: 2019 ident: 10.1016/j.commatsci.2020.110067_b0050 article-title: Active-learning and materials design: the example of high glass transition temperature polymers publication-title: MRS Commun. doi: 10.1557/mrc.2019.78 – ident: 10.1016/j.commatsci.2020.110067_b0095 – ident: 10.1016/j.commatsci.2020.110067_b0105 – ident: 10.1016/j.commatsci.2020.110067_b0110 – volume: 28 start-page: 6277 year: 2016 ident: 10.1016/j.commatsci.2020.110067_b0020 article-title: Rational co-design of polymer dielectrics for energy storage publication-title: Adv. Mater. doi: 10.1002/adma.201600377 – year: 2002 ident: 10.1016/j.commatsci.2020.110067_b0130 – year: 2009 ident: 10.1016/j.commatsci.2020.110067_b0135 – ident: 10.1016/j.commatsci.2020.110067_b0085 – ident: 10.1016/j.commatsci.2020.110067_b0165 doi: 10.1002/cmdc.200800178 – ident: 10.1016/j.commatsci.2020.110067_b0100 – ident: 10.1016/j.commatsci.2020.110067_b0060 doi: 10.1021/acs.jcim.9b00656 – volume: 6 start-page: 61 year: 2020 ident: 10.1016/j.commatsci.2020.110067_b0030 article-title: Frequency-dependent dielectric constant prediction of polymers using machine learning publication-title: Npj Comput. Mater. doi: 10.1038/s41524-020-0333-6 – ident: 10.1016/j.commatsci.2020.110067_b0115 – volume: 159 start-page: 95 year: 2018 ident: 10.1016/j.commatsci.2020.110067_b0065 article-title: A material genome approach towards exploration of zn and cd coordination complex polyester as dielectrics: design, synthesis and characterization publication-title: Polymer doi: 10.1016/j.polymer.2018.10.017 – volume: 83 start-page: 236 year: 2016 ident: 10.1016/j.commatsci.2020.110067_b0025 article-title: Advanced polymeric dielectrics for high energy density applications publication-title: Prog. Mater. Sci. doi: 10.1016/j.pmatsci.2016.05.001 – ident: 10.1016/j.commatsci.2020.110067_b0080 doi: 10.1021/acscentsci.7b00572 – year: 1975 ident: 10.1016/j.commatsci.2020.110067_b0145 – volume: 21 start-page: 785 issue: 7 year: 2018 ident: 10.1016/j.commatsci.2020.110067_b0010 article-title: Scoping the polymer genome: a roadmap for rational polymer dielectrics design and beyond publication-title: Mater. Today doi: 10.1016/j.mattod.2017.11.021 – ident: 10.1016/j.commatsci.2020.110067_b0140 doi: 10.1109/EIDWT.2011.13 – volume: 29 start-page: 9001 issue: 21 year: 2017 ident: 10.1016/j.commatsci.2020.110067_b0055 article-title: Mining materials design rules from data: the example of polymer dielectrics publication-title: Chem. Mater. doi: 10.1021/acs.chemmater.7b02027 – ident: 10.1016/j.commatsci.2020.110067_b0090 – ident: 10.1016/j.commatsci.2020.110067_b0015 doi: 10.1021/acs.jpcc.8b02913 |
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| Title | Polymer design using genetic algorithm and machine learning |
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