Article
  • Deep Learning Model for Prediction of Entanglement Molecular Weight of Polymers
  • Jihoon Park, Joona Bang , and June Huh

  • Department of Chemical and Biological Engineering, Korea University, Seoul 02841, Korea

  • 고분자 엉킴 분자량 예측을 위한 심층 학습 모델 연구
  • 박지훈 · 방준하 · 허준

  • 고려대학교 화공생명공학과

  • Reproduction, stored in a retrieval system, or transmitted in any form of any part of this publication is permitted only by written permission from the Polymer Society of Korea.

References
  • 1. Wool, R. P. Polymer Entanglements. Macromolecules 1993, 26, 1564-1569.
  •  
  • 2. Eckstein, A., Suhm, J.; Friedrich, C.; Maier, R. D.; Sassmannshausen, J.; Bochmann, M.; Mülhaupt, R. Determination of Plateau Moduli and Entanglement Molecular Weights of Isotactic, Syndiotactic, and Atactic Polypropylenes Synthesized with Metallocene Catalysts. Macromolecules 1998, 31, 1335-1340.
  •  
  • 3. Cho, K. S. Viscoelastic Measurement and Structure of Polymeric Materials. Polym. Sci. Tech. 2008, 19, 170-176.
  •  
  • 4. CROW. Polymer Database. https://polymerdatabase.com (accessed Feb 17, 2022).
  •  
  • 5. National Institute for Materials Science. PoLyInfo. https://polymer.nims.go.jp/en/ (accessed Feb 17, 2022).
  •  
  • 6. Otsuka, S.; Kuwajima, I.; Hosoya, J.; Xu, Y.; Yamazaki, M. PoLyInfo: Polymer Database for Polymeric Materials Design. In 2011 International Conference on Emerging Intelligent Data and Web Technologies, 2011, 22-29.
  •  
  • 7. Katritzky, A. R.; Lobanov, V. S.; Karelson, M. QSPR: The Correlation and Quantitative Prediction of Chemical and Physical Properties from Structure. Chem. Soc. Rev. 1995, 24, 279-287.
  •  
  • 8. Van Krevelen, D. W. Proerties of Polymers, Their Estimation and Correlation with Chemical Structure, 2nd ed; Elsevier: Amsterdam, 1976.
  •  
  • 9. Bicerano, J. Prediction of Polymer Properties, 1st ed; Marcel Dekker: New York, 1993.
  •  
  • 10. LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521, 436-444.
  •  
  • 11. Elton, D. C.; Boukouvalas, Z.; Fuge, M. D.; Chung, P. W. Deep Learning for Molecular Design-A Review of the State of the Art. Mol. Syst. Des. Eng. 2019, 4, 828-849.
  •  
  • 12. Mater, A. C.; Coote, M. L. Deep Learning in Chemistry. J. Chem. Inf. Model 2019, 59, 2545-2559.
  •  
  • 13. Doan Tran, H.; Kim, C.; Chen, L.; Chandrasekaran, A.; Batra, R.; Venkatram, S.; Kamal, D.; Lightstone, J. P.; Gurnani, R.; Shetty, P.; Ramprasad, M.; Laws, J.; Shelton, M.; Ramprasad, R. Machine-Learning Predictions of Polymer Properties with Polymer Genome. J. Appl. Phys. 2020, 128, 171104.
  •  
  • 14. Kuenneth, C.; Rajan, A. C.; Tran, H.; Chen, L.; Kim, C.; Ramprasad, R. Polymer Informatics with Multi-task Learning. Patterns 2021, 2, 100238.
  •  
  • 15. Sha, W.; Li, Y.; Tang, S.; Tian, J.; Zhao, Y.; Guo, Y.; Zhang, W.; Zhang, X.; Lu, S.; Cao, Y.; Cheng, S. Machine Learning in Polymer Informatics. InfoMat. 2021, 3, 353-361.
  •  
  • 16. Kim, C.; Batra, R.; Chen, L.; Tran, H.; Ramprasad, R. Polymer Design Using Genetic Algorithm and Machine Learning. Compu. Mater. Sci. 2021, 186, 110067.
  •  
  • 17. Mannodi-Kanakkithodi, A.; Pilania, G.; Huan, T. D.; Lookman, T.; Ramprasad, R. Machine Learning Strategy for Accelerated Design of Polymer Dielectrics. Sci. Rep. 2016, 6, 1-10.
  •  
  • 18. Cassar, D. R.; de Carvalho, A. C.; Zanotto, E. D. Predicting Glass Transition Temperatures Using Neural Networks. Acta Mater. 2018, 159, 249-256.
  •  
  • 19. Alcobaca, E.; Mastelini, S. M.; Botari, T.; Pimentel, B. A.; Cassar, D. R.; de Leon Ferreira, A. C. P.; Zanotto, E. D. Explainable Machine Learning Algorithms for Predicting Glass Transition Temperatures. Acta Mater. 2020, 188, 92-100.
  •  
  • 20. Chen, G.; Tao, L.; Li, Y. Predicting Polymers’ Glass Transition Temperature by a Chemical Language Processing Model. Polymers 2021, 13, 1898.
  •  
  • 21. Yamada, H.; Liu, C.; Wu, S.; Koyama, Y.; Ju, S.; Shiomi, J.; Morikawa, J.; Yoshida, R. Predicting Materials Properties with Little Data Using Shotgun Transfer Learning. ACS Cent. Sci. 2019, 5, 1717-1730.
  •  
  • 22. Wu, S.; Kondo, Y.; Kakimoto, M. A.; Yang, B.; Yamada, H.; Kuwajima, I.; Lambard, G.; Hongo, K.; Xu, Y.; Shiomi, J.; Schick, C.; Morikawa, J.; Yoshida, R. Machine-learning-assisted Discovery of Polymers with High Thermal Conductivity Using a Molecular Design Algorithm. Npj Comput. Mater. 2019, 5, 1-11.
  •  
  • 23. Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; Monfardini, G. The Graph Neural Network Model. IEEE Trans. Neural Netw. 2008, 20, 61-80.
  •  
  • 24. Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. Convolutional Networks on Graphs for Learning Molecular Fingerprints. Adv. Neural Inf. Process. Syst. 2015, 28.
  •  
  • 25. Ramsundar, B.; Eastman, P.; Walters, P.; Pande, V. Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more. O'Reilly Media: Sebastopol, 2019.
  •  
  • 26. Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; Dahl, G. E. Neural Message Passing for Quantum Chemistry. In International Conference on Machine Learning. PMLR 2017, 1263-1272.
  •  
  • 27. Sun, M.; Zhao, S.; Gilvary, C.; Elemento, O.; Zhou, J.; Wang, F. Graph Convolutional Networks for Computational Drug Development and Discovery. Brief. Bioinform. 2020, 21, 919-935.
  •  
  • 28. Wang, X.; Li, Z.; Jiang, M.; Wang, S.; Zhang, S.; Wei, Z. Molecule Property Prediction Based on Spatial Graph Embedding. J. Chem. Inf. Model 2019, 59, 3817-3828.
  •  
  • 29. Zhu, X.; Vondrick, C.; Fowlkes, C. C.; Ramanan, D. Do We Need More Training Data? Int. J. Comput. Vis. 2016, 119, 76-92.
  •  
  • 30. Hestness, J.; Narang, S.; Ardalani, N.; Diamos, G.; Jun, H.; Kianinejad, H.; Patwary, M. M. A.; Yang, Y.; Zhou, Y. Deep Learning Scaling is Predictable, Empirically. arXiv 2017, arXiv:1712.00409.
  •  
  • 31. Pan, S. J.; Yang, Q. A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 2009, 22, 1345-1359.
  •  
  • 32. Ruddigkeit, L.; Van Deursen, R.; Blum, L. C.; Reymond, J. L. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17. J. Chem. Inf. Model 2012, 52, 2864-2875.
  •  
  • 33. Ramakrishnan, R.; Dral, P. O.; Rupp, M.; Von Lilienfeld, O. A. Quantum Chemistry Structures and Properties of 134 Kilo Molecules. Sci. Data 2014, 1, 1-7.
  •  
  • 34. Weininger, D. SMILES, A Chemical Language anD Information System. 1. Introduction to Methodology and Encoding Rules. J. Chem. Inf. Comput. Sci. 1988, 28, 31-36.
  •  
  • 35. RDKit: Open-Source Cheminformatics Software. https://www. rdkit.org (accessed Feb 22, 2022).
  •  
  • 36. Li, Z.; Wellawatte, G. P.; Chakraborty, M.; Gandhi, H. A.; Xu, C.; White, A. D. Graph Neural Network Based Coarse-grained Mapping Prediction. Chem. Sci. 2020, 11, 9524-9531.
  •  
  • 37. Vinyals, O.; Bengio, S.; Kudlur, M. Order Matters: Sequence to Sequence for Sets. arXiv 2015, arXiv:1511.06391.
  •  
  • 38. Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv 2014, arXiv:1412.3555.
  •  
  • 39. Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; Chintala, S. Pytorch: An Imperative Style, High-performance Deep Learning Library. Adv. Neural Inf. Process. Syst. 2019, 32.
  •  
  • 40. Fey, M.; Lenssen, J. E. Fast Graph Representation Learning with PyTorch Geometric. arXiv 2019, arXiv:1903.02428.
  •  
  • 41. Kingma, D. P.; Ba, J. Adam: A Method for Stochastic Optimization. arXiv 2014, arXiv:1412.6980.
  •  
  • 42. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V., Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Duchesnay, E. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825-2830.
  •  
  • 43. Mosteller, F.; Tukey, J. W. Data Analysis, Including Statistics. Handbook of Social Psychology 1968, 2, 80-203.
  •  
  • 44. Benesty, J.; Chen, J.; Huang, Y.; Cohen, I. Pearson Correlation Coefficient. In Noise Reduction in Speech Processing, Springer: Berlin, 2009; pp 37-40.
  •  
  • 45. Hait, D.; Liang, Y. H.; Head-Gordon, M. Too Big, Too Small, or Just Right? A Benchmark Assessment of Density Functional Theory for Predicting the Spatial Extent of the Electron Density of Small Chemical Systems. J. Chem. Phys. 2021, 154, 074109.
  •  
  • 46. Sander, T.; Freyss, J.; von Korff, M.; Rufener, C. DataWarrior: an Open-source Program for Chemistry Aware Data Visualization and Analysis. J. Chem. Inf. Model. 2015, 55, 460-473.
  •  
  • 47. 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.
  •  
  • Polymer(Korea) 폴리머
  • Frequency : Bimonthly(odd)
    ISSN 0379-153X(Print)
    ISSN 2234-8077(Online)
    Abbr. Polym. Korea
  • 2023 Impact Factor : 0.4
  • Indexed in SCIE

This Article

  • 2022; 46(4): 515-522

    Published online Jul 25, 2022

  • 10.7317/pk.2022.46.4.515
  • Received on Apr 7, 2022
  • Revised on May 12, 2022
  • Accepted on May 13, 2022

Correspondence to

  • Joona Bang , and June Huh
  • Department of Chemical and Biological Engineering, Korea University, Seoul 02841, Korea

  • E-mail: joona@korea.ac.kr, junehuh@korea.ac.kr