Probabilistic Graphical Models for Predicting Properties of New Materials Based on Their Composition and Structure

  • Vusumuzi Malele North West University
  • Ashley Phala North West University

Keywords: Machine Learning, Probabilistic Graphical Models, Research on Discovering New Properties of Materials

Abstract

Probabilistic graphical model (PGMs) offer a powerful framework for modeling complex relationships between different components. By integrating information on the element composition and structural features, these models enable the inference of materials properties with a probabilistic perspective. This approach holds promising efforts towards accelerating materials discovery design, as it facilitates the predication of diverse materials characteristics, ranging from electronic and mechanical properties to thermal and optical behavior. The use of PGMs in materials science represents a sophisticated methodology for harnessing data-driven insights to guide the exploration of innovative materials with tailored functionalities. The purpose of this paper is to investigate literature for the exploitation of the data science concepts, big data and machine learning that yields computational intelligence. A literature review approach to understand the exploitation and use of computational intelligence in the leading-edge research and innovation of materials science. The findings illustrate that machine learning can be used to intricate chemical problems that otherwise would not be tractable. Leveraging PGMs presents a promising avenue for predicting the properties of new materials based on their composition and structure.

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References

Wei, J., Chu, X., Sun, X.Y., Xu, K., Deng, H.X., Chen, J., Wei, Z. and Lei, M., 2019. Machine learning in materials science. InfoMat, 1(3), pp.338-358, doi: 10.1002/inf2.12028.

Wang, A.Y.T., Murdock, R.J., Kauwe, S.K., Oliynyk, A.O., Gurlo, A., Brgoch, J., Persson, K.A. and Sparks, T.D., 2020. Machine learning for materials scientists: an introductory guide toward best practices. Chemistry of Materials, 32(12), pp.4954-4965, doi: 10.1021/acs.chemmater.0c01907.

Mueller, T., Kusne, A.G. and Ramprasad, R., 2016. Machine learning in materials science: Recent progress and emerging applications. Reviews in computational chemistry, 29, pp.186-273, doi: 10.1002/9781119148739.ch4.

Butler, K.T., Davies, D.W., Cartwright, H., Isayev, O. and Walsh, A., 2018. Machine learning for molecular and materials science. Nature, 559 (7715), pp.547-555, doi: 10.1038/s41586-018-0337-2.

Morgan, D. and Jacobs, R., 2020. Opportunities and challenges for machine learning in materials science. Annual Review of Materials Research, 50, pp.71-103, doi: 10.1146/annurev-matsci-070218-010015.

El-Shafie, M., 2023. Hydrogen production by water electrolysis technologies: A review. Results in Engineering. Natural gas, 240, p.48, doi: 10.1016/j.rineng.2023.101426.

Koller, D. and Friedman, N., 2009. Probabilistic graphical models: principles and techniques. MIT press.

Reiser, P., Neubert, M., Eberhard, A., Torresi, L., Zhou, C., Shao, C., Metni, H., van Hoesel, C., Schopmans, H., Sommer, T. and Friederich, P., 2022. Graph neural networks for materials science and chemistry. Communications Materials, 3(1), p.93, doi: 10.1038/s43246-022-00315-6.

Bhadeshia, H.K.D.H. 1999. Neural networks in materials science. ISIJ international, 39(10), pp.966-979, doi: 10.2355/isijinternational.39.966.

Ferguson, A.L., 2017. Machine learning and data science in soft materials engineering. Journal of Physics: Condensed Matter, 30(4), p.043002, doi: 10.1088/1361-648x/aa98bd.

Sucar, L.E., 2015. Probabilistic graphical models. Advances in Computer Vision and Pattern Recognition. London: Springer London. doi, 10(978), p.1, doi: 10.1007/978-3-030-61943-5.

Pernkopf, F., Peharz, R. and Tschiatschek, S., 2014. Introduction to probabilistic graphical models. In Academic Press Library in Signal Processing (Vol. 1, pp. 989-1064). Elsevier, 10.1007/978-1-4615-1539-5_2.

Martin, D. Schmidt, M.W. and Hillerbrand, R. Implementing AI Ethics in the Design of AI-assisted Rescue Robots", 2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS), West Lafayette, IN, USA, i2023, pp. 1-1, doi: 10.1109/ETHICS57328.2023.10155062.

Bagayoko, D., 2014. Understanding density functional theory (DFT) and completing it in practice. AIP Advances, 4(12), doi: 10.1063/1.4903408

Burke, K. and Wagner, L.O., 2013. DFT in a nutshell. International Journal of Quantum Chemistry, 113(2), pp.96-101, doi: 10.1002/qua.24259.

Jordan, M.I., 2003. An introduction to probabilistic graphical models.

Larrañaga, P., Karshenas, H., Bielza, C. and Santana, R., 2012. A review on probabilistic graphical models in evolutionary computation. Journal of Heuristics, 18, pp.795-819, doi: 10.1007/s10732-012-9208-4.

Frey, B.J. and Jojic, N., 2005. A comparison of algorithms for inference and learning in probabilistic graphical models. IEEE Transactions on pattern analysis and machine intelligence, 27(9), pp.1392-1416, doi: 10.1109/TPAMI.2005.169.

Ankan, A. and Panda, A., 2015. Probabilistic graphical models using python. In Proceedings of the 14th python in science conference (scipy 2015) (Vol. 10). Citeseer, doi: 10.25080/Majora-7b98e3ed-001.

Bernardo, J.M. and Smith, A.F., 2009. Bayesian theory (Vol. 405). John Wiley & Sons.

Li, Stan Z. "Markov random field models in computer vision." In Computer Vision—ECCV'94: Third European Conference on Computer Vision Stockholm, Sweden, May 2–6 1994 Proceedings, Volume II 3, pp. 361-370. Springer Berlin Heidelberg, 1994, doi: 10.1007/978-4-431-66933-3.

Published
2024-12-31
How to Cite
Malele, V., & Phala, A. (2024). Probabilistic Graphical Models for Predicting Properties of New Materials Based on Their Composition and Structure. Indonesian Journal of Data and Science, 5(3), 237-242. https://doi.org/10.56705/ijodas.v5i3.177