Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning

Fang Ding; Lun-Yang Liu; Ting-Li Liu; Yun-Qi Li; Jun-Peng Li; Zhao-Yan Sun

doi:10.1007/s10118-022-2838-6

Your Location：

Home >

Browse articles >

Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning

RESEARCH ARTICLE | Updated：2023-01-18

- Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning
  Enhanced Publication
- Chinese Journal of Polymer Science Vol. 41, Issue 3, Pages: 422-431(2023)
- Affiliations：
  
  a.State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
  b.School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, China
  c.Department of Polymer Materials and Engineering, College of Materials and Metallurgy, Guizhou University, Guiyang 550025, China
  d.State Key Laboratory of Advanced Technologies for Comprehensive Utilization of Platinum Metals, Sino-Platinum Metals Co. Ltd., Kunming 650106, China
- Author bio：
  
  yunqi@ciac.ac.cn (Y.Q.L.)
  lijunpeng@ipm.com.cn (J.P.L)
  zysun@ciac.ac.cn (Z.Y.S.)
- Funds：
- DOI：10.1007/s10118-022-2838-6
  CLC：
Scan for full text
Fang Ding, Lun-Yang Liu, Ting-Li Liu, et al. Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning. [J]. Chinese Journal of Polymer Science 41(3):422-431(2023)
DOI：

Fang Ding, Lun-Yang Liu, Ting-Li Liu, et al. Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning. [J]. Chinese Journal of Polymer Science 41(3):422-431(2023) DOI： 10.1007/s10118-022-2838-6.

摘要

Predict the mechanical properties of polyurethane elastomers and construct a benchmark dataset with conserved chemical structure-composition-processing-mechanical properties relationship.

Abstract

Bridging the gap between the computation of mechanical properties and the chemical structure of elastomers is a long-standing challenge. To fill the gap, we create a raw dataset and build predictive models for Young’s modulus, tensile strength, and elongation at break of polyurethane elastomers (PUEs). We then construct a benchmark dataset with 50.4% samples remained from the raw dataset which suffers from the intrinsic diversity problem, through a newly proposed recursive data elimination protocol. The coefficients of determination (,R,2,s) from predictions are improved from 0.73−0.78 to 0.85−0.91 based on the raw and the benchmark datasets. The fitting of stress-strain curves using the machine learning model shows a slightly better performance than that for one of the well-performed constitutive models (,e.g., the Khiêm-Itskov model). It confirmed that the black-box machine learning models are feasible to bridge the gap between the mechanical properties of PUEs and multiple factors for their chemical structures, composition, processing, and measurement settings. While accurate prediction for these curves is still a challenge. We release the raw dataset and the most representative benchmark dataset so far to call for more attention to tackle the long-standing gap problem.

关键词

Keywords

Mechanical propertiesStress-strain curvesPolyurethane elastomersMachine learningBenchmark dataset

references

Liu, T.; Liu, L.; Cui, F.; Ding, F.; Zhang, Q.; Li, Y . Predicting the performance of polyvinylidene fluoride, polyethersulfone and polysulfone filtration membranes using machine learning . J. Mater. Chem. A , 2020 . 8 21862 -21871 . DOI:10.1039/D0TA07607Dhttp://doi.org/10.1039/D0TA07607D .

Liu, L.; Chen, W.; Li, Y . A statistical study of proton conduction in nafion®-based composite membranes: prediction, filler selection and fabrication methods . J. Membr. Sci. , 2018 . 549 393 -402 . DOI:10.1016/j.memsci.2017.12.025http://doi.org/10.1016/j.memsci.2017.12.025 .

Liu, L.; Liu, T.; Ding, F.; Zhang, H.; Zheng, J.; Li, Y . Exploration of the polarization curve for proton-exchange membrane fuel cells . ACS Appl. Mater. Interfaces , 2021 . 13 58838 -58847 . DOI:10.1021/acsami.1c20289http://doi.org/10.1021/acsami.1c20289 .

Jin, K.; Luo, H.; Wang, Z.; Wang, H.; Tao, J . Composition optimization of a high-performance epoxy resin based on molecular dynamics and machine learning . Mater. Des. , 2020 . 194 108932 DOI:10.1016/j.matdes.2020.108932http://doi.org/10.1016/j.matdes.2020.108932 .

Liu, L. Y.; Chen, W. D.; Liu, T. L.; Kong, X. X.; Zheng, J. F.; Li, Y. Q . Rational design of hydrocarbon-based sulfonated copolymers for proton exchange membranes . J. Mater. Chem. A , 2019 . 7 11847 -11857 . DOI:10.1039/C9TA00688Ehttp://doi.org/10.1039/C9TA00688E .

Somarathna, H. M. C. C.; Raman, S. N.; Mohotti, D.; Mutalib, A. A.; Badri, K. H . The use of polyurethane for structural and infrastructural engineering applications: a state-of-the-art review . Constr. Build. Mater. , 2018 . 190 995 -1014 . DOI:10.1016/j.conbuildmat.2018.09.166http://doi.org/10.1016/j.conbuildmat.2018.09.166 .

Opris, D. M . Polar elastomers as novel materials for electromechanical actuator applications . Adv. Mater. , 2018 . 30 1703678 DOI:10.1002/adma.201703678http://doi.org/10.1002/adma.201703678 .

Xiao, R.; Huang, W. M . Heating/solvent responsive shape-memory polymers for implant biomedical devices in minimally invasive surgery: current status and challenge . Macromol. Biosci. , 2020 . 20 e2000108 DOI:10.1002/mabi.202000108http://doi.org/10.1002/mabi.202000108 .

Shi, R.; Chen, D.; Liu, Q.; Wu, Y.; Xu, X.; Zhang, L.; Tian, W . Recent advances in synthetic bioelastomers . Int. J. Mol. Sci. , 2009 . 10 4223 -4256 . DOI:10.3390/ijms10104223http://doi.org/10.3390/ijms10104223 .

Utrera-Barrios, S.; Verdejo, R.; Lopez-Manchado, M. A.; Santana, M. H . Evolution of self-healing elastomers, from extrinsic to combined intrinsic mechanisms: a review . Mater. Horizons , 2020 . 7 2882 -2902 . DOI:10.1039/D0MH00535Ehttp://doi.org/10.1039/D0MH00535E .

Ma, Z. P.; Li, H.; Jing, X.; Liu, Y. J.; Mi, H. Y . Recent advancements in self-healing composite elastomers for flexible strain sensors: materials, healing systems, and features . Sensors Actuators A-Phys. , 2021 . 329 112800 DOI:10.1016/j.sna.2021.112800http://doi.org/10.1016/j.sna.2021.112800 .

Sui, T.; Baimpas, N.; Dolbnya, I. P.; Prisacariu, C.; Korsunsky, A. M . Multiple-length-scale deformation analysis in a thermoplastic polyurethane . Nat. Commun. , 2015 . 6 6583 DOI:10.1038/ncomms7583http://doi.org/10.1038/ncomms7583 .

Ding, F.; Liu, T.; Zhang, H.; Liu, L.; Li, Y . Stress-strain curves for polyurethane elastomers: a statistical assessment of constitutive models . J. Appl. Polym. Sci. , 2021 . 138 e51269 DOI:10.1002/app.51269http://doi.org/10.1002/app.51269 .

He, H.; Zhang, Q.; Zhang, Y.; Chen, J.; Zhang, L.; Li, F . A comparative study of 85 hyperelastic constitutive models for both unfilled rubber and highly filled rubber nanocomposite material . Nano Mater. Sci. , 2021 . 4 64 -82. .

Ma, R.; Luo, T . Pi1m: A benchmark database for polymer informatics . J. Chem. Inf. Model. , 2020 . 60 4684 -4690 . DOI:10.1021/acs.jcim.0c00726http://doi.org/10.1021/acs.jcim.0c00726 .

Otsuka, S.; Kuwajima, I.; Hosoya, J.; Xu, Y.; Yamazaki, M . Polyinfo: Polymer database for polymeric materials design . Proceedings of the 2011 International Conference on Emerging Intelligent Data and Web Technologies , 2011 . 22 -29. .

Nozaki, S.; Masuda, S.; Kamitani, K.; Kojio, K.; Takahara, A.; Kuwarnura, G.; Hasegawa, D.; Moorthi, K.; Mita, K.; Yamasaki, S . Superior properties of polyurethane elastomers synthesized with aliphatic diisocyanate bearing a symmetric structure . Macromolecules , 2017 . 50 1008 -1015 . DOI:10.1021/acs.macromol.6b02044http://doi.org/10.1021/acs.macromol.6b02044 .

Hu, J.; Mo, R.; Sheng, X.; Zhang, X . A self-healing polyurethane elastomer with excellent mechanical properties based on phase-locked dynamic imine bonds . Polym. Chem. , 2020 . 11 2585 -2594 . DOI:10.1039/D0PY00151Ahttp://doi.org/10.1039/D0PY00151A .

Chen, T.; Guestrin, C . Xgboost: A scalable tree boosting system . Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2016 . 785 -794 . DOI:10.1145/2939672.2939785http://doi.org/10.1145/2939672.2939785 .

Landrum, G. Rdkit: Open-source cheminformatics software. 2021. https://rdkit.org/

Van Krevelen, D. W.; Te Nijenhuis, K., in Properties of polymers (fourth edition), Elsevier. Amsterdam, 2009, 189−227.

Fedors, R. F. A method for estimating both the solubility parameters and molar volumes of liquids . Supplement . Polym. Eng. Sci. , 1974 . 14 472 -472 . DOI:10.1002/pen.760140611http://doi.org/10.1002/pen.760140611 .

Zhang, H.; Ding, F.; Liu, T. L.; Liu, L. Y.; Li, Y. Q . Additivity of the mechanical properties for acrylonitrile-butadiene-styrene resins . J. Appl. Polym. Sci. , 2022 . 139 e51923 DOI:10.1002/app.51923http://doi.org/10.1002/app.51923 .

Guyon, I.; Weston, J.; Barnhill, S.; Vapnik, V . Gene selection for cancer classification using support vector machines . Mach. Learn. , 2002 . 46 389 -422 . DOI:10.1023/A:1012487302797http://doi.org/10.1023/A:1012487302797 .

Breiman, L . Random forests . Mach. Learn. , 2001 . 45 5 -32 . DOI:10.1023/A:1010933404324http://doi.org/10.1023/A:1010933404324 .

Smola, A. J.; Scholkopf, B . A tutorial on support vector regression . Stat. Comput. , 2004 . 14 199 -222 . DOI:10.1023/B:STCO.0000035301.49549.88http://doi.org/10.1023/B:STCO.0000035301.49549.88 .

Rumelhart, D. E.; Hinton, G. E.; Williams, R. J . Learning representations by back-propagating errors . Nature , 1986 . 323 533 -536 . DOI:10.1038/323533a0http://doi.org/10.1038/323533a0 .

Fix, E.; Hodges, J. L . Discriminatory analysis-nonparametric discrimination - consistency properties . Int. Stat. Rev. , 1989 . 57 238 -247 . DOI:10.2307/1403797http://doi.org/10.2307/1403797 .

Altman, N. S . An introduction to kernel and nearest-neighbor nonparametric regression . Am. Stat. , 1992 . 46 175 -185. .

Pugar, J. A.; Gang, C.; Huang, C.; Haider, K. W.; Washburn, N. R . Predicting young’s modulus of linear polyurethane and polyurethane–polyurea elastomers: bridging length scales with physicochemical modeling and machine learning . ACS Appl. Mater. Interfaces , 2022 . 14 16568 -16581 . DOI:10.1021/acsami.1c24715http://doi.org/10.1021/acsami.1c24715 .

Ertem, S. P.; Yilgor, E.; Kosak, C.; Wilkes, G. L.; Zhang, M. Q.; Yilgor, I . Effect of soft segment molecular weight on tensile properties of poly(propylene oxide) based polyurethaneureas . Polymer , 2012 . 53 4614 -4622 . DOI:10.1016/j.polymer.2012.08.020http://doi.org/10.1016/j.polymer.2012.08.020 .

Cordero, J . A.; He K.; Janya K.; Echigo S.; Itoh S. Predicting formation of haloacetic acids by chlorination of organic compounds using machine-learning-assisted quantitative structure-activity relationships . J. Hazard. Mater. , 2021 . 408 124466 DOI:10.1016/j.jhazmat.2020.124466http://doi.org/10.1016/j.jhazmat.2020.124466 .

Yang, S.; Wang, S.; Du, X.; Du, Z.; Cheng, X.; Wang, H . Mechanically robust self-healing and recyclable flame-retarded polyurethane elastomer based on thermoreversible crosslinking network and multiple hydrogen bonds . Chem. Eng. J. , 2020 . 391 123544 DOI:10.1016/j.cej.2019.123544http://doi.org/10.1016/j.cej.2019.123544 .

Prasanna, S.; Doerksen, R. J . Topological polar surface area: A useful descriptor in 2d-qsar . Curr. Med. Chem. , 2009 . 16 21 -41 . DOI:10.2174/092986709787002817http://doi.org/10.2174/092986709787002817 .

Labute, P . A widely applicable set of descriptors . J. Mol. Graph. Model. , 2000 . 18 464 -477 . DOI:10.1016/S1093-3263(00)00068-1http://doi.org/10.1016/S1093-3263(00)00068-1 .

Pugar, J. A.; Childs, C. M.; Huang, C.; Haider, K. W.; Washburn, N. R . Elucidating the physicochemical basis of the glass transition temperature in linear polyurethane elastomers with machine learning . J. Phys. Chem. B , 2020 . 124 9722 -9733 . DOI:10.1021/acs.jpcb.0c06439http://doi.org/10.1021/acs.jpcb.0c06439 .

Menon, A.; Thompson-Colon, J. A.; Washburn, N. R . Hierarchical machine learning model for mechanical property predictions of polyurethane elastomers from small datasets . Front. Mater. , 2019 . 6 87 DOI:10.3389/fmats.2019.00087http://doi.org/10.3389/fmats.2019.00087 .

He, Y.; Xie, D.; Zhang, X . The structure, microphase-separated morphology, and property of polyurethanes and polyureas . J. Mater. Sci. , 2014 . 49 7339 -7352 . DOI:10.1007/s10853-014-8458-yhttp://doi.org/10.1007/s10853-014-8458-y .

Sonnenschein, M. F.; Guillaudeu, S. J.; Landes, B. G.; Wendt, B. L . Comparison of adipate and succinate polyesters in thermoplastic polyurethanes . Polymer , 2010 . 51 3685 -3692 . DOI:10.1016/j.polymer.2010.06.012http://doi.org/10.1016/j.polymer.2010.06.012 .

Shin, J.; Matsushima, H.; Chan, J. W.; Hoyle, C. E . Segmented polythiourethane elastomers through sequential thiol-ene and thiol-isocyanate reactions . Macromolecules , 2009 . 42 3294 -3301 . DOI:10.1021/ma8026386http://doi.org/10.1021/ma8026386 .

Falco, G.; Simonin, L.; Pensec, S.; Dalmas, F.; Chenal, J. M.; Bouteiller, L.; Chazeau, L . Linear and nonlinear viscoelastic properties of segmented silicone-urea copolymers: Influence of the hard segment structure . Polymer , 2020 . 186 122041 DOI:10.1016/j.polymer.2019.122041http://doi.org/10.1016/j.polymer.2019.122041 .

Rogulska, M.; Kultys, A.; Pikus, S. . Studies on thermoplastic polyurethanes based on new diphenylethane-derivative diols. Iii. The effect of molecular weight and structure of soft segment on some properties of segmented polyurethanes . J. Appl. Polym. Sci. , 2008 . 110 1677 -1689 . DOI:10.1002/app.28583http://doi.org/10.1002/app.28583 .

Kim, H. D.; Lee, T. J.; Huh, J. H.; Lee, D. J . Preparation and properties of segmented thermoplastic polyurethane elastomers with two different soft segments . J. Appl. Polym. Sci. , 1999 . 73 345 -352 . DOI:10.1002/(SICI)1097-4628(19990718)73:3<345::AID-APP5>3.0.CO;2-Thttp://doi.org/10.1002/(SICI)1097-4628(19990718)73:3<345::AID-APP5>3.0.CO;2-T .

Liao, T.; Yang, X.; Zhao, X. T.; Tang, Y. J.; Jiang, Z. Y.; Men, Y. F . Gaussian and non-gaussian distributions of fracture properties in tensile stretching of high-density polyethylene . Macromolecules , 2021 . 54 8860 -8874 . DOI:10.1021/acs.macromol.1c00944http://doi.org/10.1021/acs.macromol.1c00944 .

Tang, H.; Cui, F.; Liu, L.; Li, Y . Predictive models for tyrosinase inhibitors: challenges from heterogeneous activity data determined by different experimental protocols . Comput. Biol. Chem. , 2018 . 73 79 -84 . DOI:10.1016/j.compbiolchem.2018.02.007http://doi.org/10.1016/j.compbiolchem.2018.02.007 .

Cereto-Massague, A.; Ojeda, M . J.; Valls C.; Mulero M.; Garcia-Vallve S.; Pujadas G. Molecular fingerprint similarity search in virtual screening . Methods , 2015 . 71 58 -63 . DOI:10.1016/j.ymeth.2014.08.005http://doi.org/10.1016/j.ymeth.2014.08.005 .

Durant, J. L.; Leland, B. A.; Henry, D. R.; Nourse, J. G . Reoptimization of mdl keys for use in drug discovery . J. Chem. Inf. Comput. Sci. , 2002 . 42 1273 -1280 . DOI:10.1021/ci010132rhttp://doi.org/10.1021/ci010132r .

Zhang, H.; Ding, F.; Liu, T.; Liu, L.; Li, Y . Additivity of the mechanical properties for acrylonitrile-butadiene-styrene resins . J. Appl. Polym. Sci. , 2021 . 139 e51923 .

Kim, B. K.; Lee, S. Y.; Xu, M . Polyurethanes having shape memory effects . Polymer , 1996 . 37 5781 -5793 . DOI:10.1016/S0032-3861(96)00442-9http://doi.org/10.1016/S0032-3861(96)00442-9 .

Rahmawati, R.; Nozaki, S.; Kojio, K.; Takahara, A.; Shinohara, N.; Yamasaki, S . Microphase-separated structure and mechanical properties of cycloaliphatic diisocyanate-based thiourethane elastomers . Polym. J. , 2018 . 51 265 -273. .

Oprea, S.; Timpu, D.; Oprea, V . Design-properties relationships of polyurethanes elastomers depending on different chain extenders structures . J. Polym. Res. , 2019 . 26 117 DOI:10.1007/s10965-019-1777-6http://doi.org/10.1007/s10965-019-1777-6 .

Khiem, V. N.; Itskov, M . Analytical network-averaging of the tube model: rubber elasticity . J. Mech. Phys. Solids , 2016 . 95 254 -269 . DOI:10.1016/j.jmps.2016.05.030http://doi.org/10.1016/j.jmps.2016.05.030 .

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method

Data and Machine Learning in Polymer Science

Nanoparticle-filled ABC Star Triblock Copolymers: A Dissipative Particle Dynamics Study

Investigating the Absorption and Desorption Behavior of Methylene Chloride in the Poly(aryl ether ketone) Film with Different Crystallinities

Related Author

No data

Related Institution

School of Chemistry, Center of Soft Matter Physics and Its Applications, Beihang University

Shanghai Key Laboratory of Advanced Polymeric Materials, School of Materials Science and Engineering, East China University of Science and Technology

School of Chemistry, Beihang University

The State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University

Department of Physics, Shanghai Technical Institute of Electronics & Information

⁰