Leveraging Machine Learning for AI-Powered Polymer Design and Property Prediction

Authors

  • Sri Charan Yarlagadda

Keywords:

artificial intelligence, molecular, prospective, nonlinear

Abstract

The combination of artificial intelligence (AI) and machine learning (ML) is transforming the field of polymer science, particularly in the development and optimization of polymers. In this paper, we discuss the profound effect that AI driven methods and sophisticated ML algorithms are having on the design of polymers and the prediction of their properties. With access to large datasets and computational power, researchers are able to achieve exceptional accuracy in forecasting polymer behaviors that were previously unimaginable. Deep learning, ensemble methods, and generative models are revealing the complex, nonlinear relationships between a polymer's molecular structure and its macroscopic properties. Furthermore, high throughput simulations and automated optimization—enabled by AI—are speeding up material discovery and allowing researchers to fine tune polymer performance in ways that were not possible before. This comprehensive study delves into the recent progress, real world applications, and prospective research, underscoring the transformative role of AI and ML in polymer science and engineering.

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References

Flory, P. J. (1953). Principles of Polymer Chemistry. Cornell University Press.

Gormley, A. J., & Webb, M. A. (2021). Machine Learning in Combinatorial Polymer Chemistry. Nature Reviews Materials, 6, 642–644.

Jha, D., Ward, L., Paul, A., Liao, W.-K., Wolverton, C., Choudhary, A., & Agrawal, A. (2018). ElemNet: Deep learning the chemistry of materials from only elemental composition. Scientific Reports, 8, 17593.

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (2017). Optimization by Simulated Annealing. Science, 220(4598), 671-680.

Amamoto, Y. (2022). Data-driven approaches for structure-property relationships in polymer science for prediction and understanding. Polymer Journal, 54, 957–967.

Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014). Quantum Chemistry Structures and Properties of 134 Kilo Molecules. Scientific Data, 2, 150022.

Rubinstein, M., & Colby, R. H. (2003). Polymer Physics. Oxford University Press.

López, C. (2023). Artificial Intelligence and Advanced Materials. Advanced Materials, 35(23).

Xie, T., & Grossman, J. C. (2018). Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical Review Letters, 120(14), 145301.

Reiser, P., Neubert, M., Eberhard, A., et al. (2022). Graph neural networks for materials science and chemistry. Communications Materials, 3, 93.

Liu, B., Li, S., & Hu, J. (2004). Technological advances in high-throughput screening. American Journal of Pharmacogenomics, 4(4), 263-276.

Goodfellow, I. (2016). NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint, 10.48550/arXiv:1701.00160.

Hu, J., Li, M., & Gao, P. (2019). MATGANIP: Learning to discover the structure-property relationship in Perovskites with generative adversarial networks. arXiv preprint, 10.48550/arXiv:1910.09003.

Menon, D., & Ranganathan, R. (2022). A Generative Approach to Materials Discovery, Design, and Optimization. ACS Omega, 7(30), 25958-25973.

Cerchia, C., & Lavecchia, A. (2023). New avenues in artificial-intelligence-assisted drug discovery. Drug Discovery Today, 28(4), 103516.

Himanen, L., Wolverton, C., & Agrawal, A. (2019). Data-driven materials science: Status, challenges, and perspectives. Advanced Science, 6(21), 1900808.

Yang, Z., Zhong, W., Zhao, L., & Chen, C. M. (2022). MGraphDTA: Deep multiscale graph neural network for explainable drug-target binding affinity prediction. Chemical Science, 13(3), 816-833.

Kim, C., Batra, R., Chen, L., Tran, H., & Ramprasad, R. (2021). Polymer design using genetic algorithm and machine learning. Computational Materials Science, 186, 110067.

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Published

06.08.2024

How to Cite

Sri Charan Yarlagadda. (2024). Leveraging Machine Learning for AI-Powered Polymer Design and Property Prediction. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 790 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6999

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Section

Research Article