Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie

• URL: http://arxiv.org/abs/2601.12554v1
• Date: Sun, 18 Jan 2026 19:36:10 GMT
• Title: Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
• Authors: Iman Peivaste, Salim Belouettar, Francesco Mercuri, Nicholas Fantuzzi, Hamidreza Dehghani, Razieh Izadi, Halliru Ibrahim, Jakub Lengiewicz, Maël Belouettar-Mathis, Kouider Bendine, Ahmed Makradi, Martin Hörsch, Peter Klein, Mohamed El Hachemi, Heinz A. Preisig, Yacine Rezgui, Natalia Konchakova, Ali Daouadji,
• Abstract summary: AI is becoming an essential competency for materials researchers.<n>We survey the spectrum of machine learning approaches, including CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models.<n>The review also examines the pivotal role of data in this field, emphasizing how effective representation and featurization strategies underpin the performance of machine learning models.
• Score: 0.28279056210896714
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Artificial Intelligence is rapidly transforming materials science and engineering, offering powerful tools to navigate complexity, accelerate discovery, and optimize material design in ways previously unattainable. Driven by the accelerating pace of algorithmic advancements and increasing data availability, AI is becoming an essential competency for materials researchers. This review provides a comprehensive and structured overview of the current landscape, synthesizing recent advancements and methodologies for materials scientists seeking to effectively leverage these data-driven techniques. We survey the spectrum of machine learning approaches, from traditional algorithms to advanced deep learning architectures, including CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models such as Gaussian Processes for uncertainty quantification. The review also examines the pivotal role of data in this field, emphasizing how effective representation and featurization strategies, spanning compositional, structural, image-based, and language-inspired approaches, combined with appropriate preprocessing, fundamentally underpin the performance of machine learning models in materials research. Persistent challenges related to data quality, quantity, and standardization, which critically impact model development and application in materials science and engineering, are also addressed.

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