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Advances in materials informatics: a review

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Abstract

Materials informatics (MI) is aimed to accelerate the materials discovery using computational intelligence and data science. Progress of MI depends on the strength of database and artificial intelligence protocols comprising machine learning (ML) and deep learning (DL) frameworks. Conventional ML models are simple and interpretable, relying on statistical techniques and algorithms to learn patterns and make predictions with limited data. Conversely, DL, an advancement of ML, employs mathematical neural networks to automatically extract features and handle intricate data at the cost of data size and computational complexity. This work aims to provide a state-of-the-art understanding of the tools, data sources and techniques used in MI and their benefits and challenges. We evaluate the growth of MI through its subfields and track the main path of its advancement for artificial intelligence-driven materials discovery. The advancements in computational intelligence via machine learning and deep learning algorithms in different fields of materials science are discussed. As a specific example, understanding of materials properties using microstructural images is reviewed. Future demands and research prospects in materials science utilizing materials informatics have also been comprehensively analyzed.

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Acknowledgements

This work was supported by the Research and Innovation Department of Universiti Malaysia Pahang through the First-in-the-world grant RDU223101, Post-graduate research scheme grant PGRS220373 and Doctoral research scheme grant UMP.20.03/13/13.14/1.

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Authors and Affiliations

  1. Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26300, Kuantan, Pahang, Malaysia

    Dawn Sivan, Izan Izwan Misnon & Rajan Jose

  2. Center for Advanced Intelligent Materials, Universiti Malaysia Pahang Al-Sultan Abdullah, 26300, Kuantan, Pahang, Malaysia

    Dawn Sivan, Izan Izwan Misnon & Rajan Jose

  3. Department of Future Studies, University of Kerala, Kariavattom, Thiruvananthapuram, Kerala, 695581, India

    K. Satheesh Kumar

  4. School of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 25000, Pekan, Malaysia

    Aziman Abdullah

  5. Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Bandar Seri Begawan, BE1410, Brunei

    Veena Raj

  6. Department of Mechanical Engineering, Centre for Nanotechnology and Sustainability, Faculty of Engineering, National University of Singapore, Singapore, 117576, Singapore

    Seeram Ramakrishna

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D.S., R.J., A.A., S.K. and S.R. designed and discussed the content of the review. D.S., R.J., V.R. and I.M. wrote the review. All the authors reviewed and edited the manuscript before submission.

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Sivan, D., Satheesh Kumar, K., Abdullah, A. et al. Advances in materials informatics: a review. J Mater Sci 59, 2602–2643 (2024). https://doi.org/10.1007/s10853-024-09379-w

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