An Intelligent Manufacturing Platform of Polymers: Polymeric Material Genome Engineering

Liang Gao; Liquan Wang; Jiaping Lin; Lei Du

doi:10.1016/j.eng.2023.01.018

PDF(1137 KB)

Engineering ›› 2023, Vol. 27 ›› Issue (8) : 31-36. DOI: 10.1016/j.eng.2023.01.018

Research

Perspective

An Intelligent Manufacturing Platform of Polymers: Polymeric Material Genome Engineering

Author information +

History +

Abstract

Polymeric materials with excellent performance are the foundation for developing high-level technology and advanced manufacturing. Polymeric material genome engineering (PMGE) is becoming a vital platform for the intelligent manufacturing of polymeric materials. However, the development of PMGE is still in its infancy, and many issues remain to be addressed. In this perspective, we elaborate on the PMGE concepts, summarize the state-of-the-art research and achievements, and highlight the challenges and prospects in this field. In particular, we focus on property estimation approaches, including property proxy prediction and machine learning prediction of polymer properties. The potential engineering applications of PMGE are discussed, including the fields of advanced composites, polymeric materials for communications, and integrated circuits.

Graphical abstract

Keywords

Polymeric materials / Materials genome approach / Machine learning / Property prediction / Rational design

Cite this article

EndNote

Ris (Procite)

Bibtex

Download citation ▾

Liang Gao, Liquan Wang, Jiaping Lin, Lei Du. An Intelligent Manufacturing Platform of Polymers: Polymeric Material Genome Engineering. Engineering, 2023, 27(8): 31‒36 https://doi.org/10.1016/j.eng.2023.01.018

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	W.L. Yuan, L. He, G.H. Tao, J.M. Shreeve. Materials-genome approach to energetic materials. Acc Mater Res, 2 (9) (2021), pp. 692-696

[2]	S. Du, S. Zhang, L. Wang, J. Lin, L. Du. Polymer genome approach: a new method for research and development of polymers. Acta Polym Sin, 53 (6) (2022), pp. 592-607. [Chinese]

[3]	J. Xie, Y. Su, D. Zhang, Q. Feng. A vision of materials genome engineering in China. Engineering, 10 (2022), pp. 10-12

[4]	H. Doan Tran, C. Kim, L. Chen, A. Chandrasekaran, R. Batra, S. Venkatram, et al. Machine-learning predictions of polymer properties with polymer genome. J Appl Phys, 128 (17) (2020), Article 171104

[5]	C. Gao, X. Min, M. Fang, T. Tao, X. Zheng, Y. Liu, et al. Innovative materials science via machine learning. Adv Funct Mater, 32 (1) (2022), Article 2108044

[6]	B.A. Rizkin, R.L. Hartman. Supervised machine learning for prediction of zirconocene-catalyzed α-olefin polymerization. Chem Eng Sci, 210 (2019), Article 115224

[7]	P. Xu, H. Chen, M. Li, W. Lu. New opportunity: machine learning for polymer materials design and discovery. Adv Theory Simul, 5 (5) (2022), Article 2100565

[8]	A. Agrawal, A. Choudhary. Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL Mater, 4 (5) (2016), Article 053208

[9]	C. Wang, H. Fu, L. Jiang, D. Xue, J. Xie. A property-oriented design strategy for high performance copper alloys via machine learning. npj Comput Mater, 5 (2019), Article 87

[10]	J. Xiong, S.Q. Shi, T.Y. Zhang. Machine learning of phases and mechanical properties in complex concentrated alloys. J Mater Sci Technol, 87 (2021), pp. 133-142

[11]	J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 596 (7873) (2021), pp. 583-589

[12]	H. Zhao, X. Li, Y. Zhang, L.S. Schadler, W. Chen, L.C. Brinson. Perspective: NanoMine: a material genome approach for polymer nanocomposites analysis and design. APL Mater, 4 (5) (2016), Article 053204

[13]	A. Mannodi-Kanakkithodi, A. Chandrasekaran, C. Kim, T.D. Huan, G. Pilania, V. Botu, et al. Scoping the polymer genome: a roadmap for rational polymer dielectrics design and beyond. Mater Today, 21 (7) (2018), pp. 785-796

[14]	V. Sharma, C. Wang, R.G. Lorenzini, R. Ma, Q. Zhu, D.W. Sinkovits, et al. Rational design of all organic polymer dielectrics. Nat Commun, 5 (1) (2014), Article 4845

[15]	J. Zhu, M. Chu, Z. Chen, L. Wang, J. Lin, L. Du. Rational design of heat-resistant polymers with low curing energies by a materials genome approach. Chem Mater, 32 (11) (2020), pp. 4527-4535

[16]	G. Gao, S. Zhang, L. Wang, J. Lin, H. Qi, J. Zhu, et al. Developing highly tough, heat-resistant blend thermosets based on silicon-containing arylacetylene: a material genome approach. ACS Appl Mater Interfaces, 12 (24) (2020), pp. 27587-27597

[17]	S. Zhang, S. Du, L. Wang, J. Lin, L. Du, X. Xu, et al. Design of silicon-containing arylacetylene resins aided by machine learning enhanced materials genome approach. Chem Eng J, 448 (15) (2022), Article 137643

[18]	A. Mannodi-Kanakkithodi, G. Pilania, T.D. Huan, T. Lookman, R. Ramprasad. Machine learning strategy for accelerated design of polymer dielectrics. Sci Rep, 6 (1) (2016), Article 20952

[19]	L. Chen, C. Kim, R. Batra, J.P. Lightstone, C. Wu, Z. Li, et al. Frequency-dependent dielectric constant prediction of polymers using machine learning. npj Comput Mater, 6 (2020), Article 61

[20]	D. Weininger. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci, 28 (1) (1988), pp. 31-36

[21]	Z. Wu, B. Ramsundar, E.N. Feinberg, J. Gomes, C. Geniesse, A.S. Pappu, et al. MoleculeNet: a benchmark for molecular machine learning. Chem Sci, 9 (2) (2018), pp. 513-530

[22]	X. Song, L. Lv, W. Sun, J. Zhang. A radial basis function-based multi-fidelity surrogate model: exploring correlation between high-fidelity and low-fidelity models. Struct Multidiscipl Optim, 60 (3) (2019), pp. 965-981

[23]	M. Raissi, P. Perdikaris, G.E. Karniadakis. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys, 378 (2019), pp. 686-707

[24]	R. van de Schoot, S. Depaoli, R. King, B. Kramer, K. Märtens, M.G. Tadesse, et al. Bayesian statistics and modelling. Nat Rev Methods Primers, 1 (1) (2021), Article 1

[25]	T.S. Lin, C.W. Coley, H. Mochigase, H.K. Beech, W. Wang, Z. Wang, et al. BigSMILES: a structurally-based line notation for describing macromolecules. ACS Cent Sci, 5 (9) (2019), pp. 1523-1531

[26]	Y. Hu, W. Zhao, L. Wang, J. Lin, L. Du. Machine-learning-assisted design of highly tough thermosetting polymers. ACS Appl Mater Interfaces, 14 (49) (2022), pp. 55004-55016

[27]	J.G. Ethier, R.K. Casukhela, J.J. Latimer, M.D. Jacobsen, A.B. Shantz, R.A. Vaia. Deep learning of binary solution phase behavior of polystyrene. ACS Macro Lett, 10 (6) (2021), pp. 749-754

[28]	P. Shetty, R. Ramprasad. Machine-guided polymer knowledge extraction using natural language processing: the example of named entity normalization. J Chem Inf Model, 61 (11) (2021), pp. 5377-5385

[29]	S. Wu, Y. Kondo, M. Kakimoto, B. Yang, H. Yamada, I. Kuwajima, et al. Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm. npj Comput Mater, 5 (2019), Article 66