Download pdf
Applications of machine learning in materials science: From a methodological point of view
- 1 University of Wisconsin-Madison
* Author to whom correspondence should be addressed.
Abstract
Discovering new functional materials with certain properties using high-throughout methods is of vital importance for materials science. The advancement of the machine learning method has provided a new, more efficient pathway for this procedure. Traditional trial-and-error methods have been supplanted by in-silico simulations, which facilitate rapid material discovery. Machine learning (ML) has further accelerated this process by uncovering patterns and relationships within complex computational data sets, thus enhancing predictive abilities for material properties and performance. This integration of computational methods and machine learning holds great potential, promising a future where material limitations are overcome, catalyzing technological breakthroughs. Furthermore, ML also inspired advancements in fields like crystallography and metallurgy, and enhancing energy storage materials. Despite challenges in model interpretability, overfitting, and data quality, ML presents an exciting evolution toward a data-driven discipline. Ultimately, ML promises to reduce the time from materials discovery to deployment, turning material limitations into catalysts for innovation.
Keywords
Machine Learning, Material Science and Engineering, Density Functional Theory, Neural Network
[1]. Song, Yaxin, Xudong Wang, Houchang Li, Yanjun He, Zilong Zhang, and Jiandong Huang. “Mixture Optimization of Cementitious Materials Using Machine Learning and Metaheuristic Algorithms: State of the Art and Future Prospects.”Materials 15, no. 21 (November 6, 2022): 7830.
[2]. Sahimi, Muhammad, and Pejman Tahmasebi. “Reconstruction, Optimization, and Design of Heterogeneous Materials and Media: Basic Principles, Computational Algorithms, and Applications.” Physics Reports 939 (December 2021): 1–82.
[3]. 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.
[4]. Zhang, Linfeng, Jiequn Han, Han Wang, Roberto Car, and Weinan E. “Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics.” Physical Review Letters 120, no. 14 (April 4, 2018): 143001.
[5]. Sch¨utt, Kristof T., Huziel E. Sauceda, Pieter-Jan Kindermans, Alexandre Tkatchenko, and Klaus-Robert M¨uller. “SchNet - a Deep Learning Architecture for Molecules and Materials.” The Journal of Chemical Physics 148, no. 24 (June 28, 2018): 241722.
[6]. Ward, L., Agrawal, A., Choudhary, A., Wolverton, C. (2016). A general-purpose machine learning framework for predicting properties of inorganic materials. Npj Computational Materials, 2(1), 16028.
[7]. S.F. Fang, M.P. Wang, W.H. Qi, F. Zheng, Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials, Comp. Mater. Sci. 44 (2008) 647–655.
[8]. Desu, R. K., Guntuku, S. C., B, A., Gupta, A. K. (2014). Support Vector Regression based Flow Stress Prediction in Austenitic Stainless Steel 304. Procedia Materials Science, 6, 368–375.
[9]. Olatunji, Sunday O., and Taoreed O. Owolabi. “Modeling the Band Gap of Spinel Nano-Ferrite Material Using a Genetic Algorithm Based Support Vector Regression Computational Method.” International Journal of Materials Research 114, no. 3 (March 28, 2023): 161–74.
[10]. Owolabi, Taoreed O., and Mohd Amiruddin Abd Rahman. “Energy Band Gap Modeling of Doped Bismuth Ferrite Multifunctional Material Using Gravitational Search Algorithm Optimized Support Vector Regression.” Crystals 11, no. 3 (February 28, 2021): 246.
[11]. Nikoo, M., Torabian Moghadam, F., Sadowski, Ł. (2015). Prediction of Concrete. Compressive Strength by Evolutionary Artificial Neural Networks. Advances in Materials Science and Engineering, 2015, 1–8.
[12]. Altinkok, N., Koker, R. (2005). Mixture and pore volume fraction estimation in Al2O3/SiC ceramic cake using artificial neural networks. Materials & Design, 26(4), 305–311.
[13]. Kujawi´nska, A., Rogalewicz, M., & Diering, M. (2016). Application of Expectation. Maximization Method for Purchase Decision-Making Support in Welding Branch. Management and Production Engineering Review, 7(2), 29–33.
[14]. Li, C., Ostadhassan, M., Abarghani, A., Fogden, A., & Kong, L. (2019). Multi-scale evaluation of mechanical properties of the Bakken shale. Journal of Materials Science, 54(3), 2133–2151.
[15]. Ward, Logan. “A General-Purpose Machine Learning Framework for Predicting.” Npj. Computational Materials, 2016.
[16]. Ghouchan Nezhad Noor Nia, Raheleh, Mehrdad Jalali, and Mahboobeh Houshmand. “A Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys.” Applied Sciences 12, no. 16 (August 10, 2022): 8021.
[17]. Mitra, Rahul, Anurag Bajpai, and Krishanu Biswas. “Machine Learning Based Approach for Phase Prediction in High Entropy Borides.” Ceramics International 48, no. 12 (June 2022): 16695–706.
[18]. Wen, Cheng, Yan Zhang, Changxin Wang, Dezhen Xue, Yang Bai, Stoichko Antonov, Lanhong Dai, Turab Lookman, and Yanjing Su. “Machine Learning Assisted Design of High Entropy Alloys with Desired Property.” Acta Materialia 170 (May 2019): 109–17.
[19]. Hasan, Md Syam, Amir Kordijazi, Pradeep K. Rohatgi, and Michael Nosonovsky. “Triboinformatic Modeling of Dry Friction and Wear of Aluminum Base Alloys Using Machine Learning Algorithms.” Tribology International 161 (September 2021): 107065.
[20]. Theeda, S, B B Ravichander, S H Jagdale, and G Kumar. “OPTIMIZATION OF LASER PROCESS PARAMETERS USING MACHINE LEARNING ALGORITHMS AND PERFORMANCE COMPARISON,” n.d.
Cite this article
Liu,Q. (2024). Applications of machine learning in materials science: From a methodological point of view. Applied and Computational Engineering,85,262-271.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 4th International Conference on Materials Chemistry and Environmental Engineering
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).