Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
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arXiv
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| Format: | Preprint |
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2026
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| author | Peivaste, Iman Belouettar, Salim Mercuri, Francesco Fantuzzi, Nicholas Dehghani, Hamidreza Izadi, Razieh Ibrahim, Halliru Lengiewicz, Jakub Belouettar-Mathis, Maël Bendine, Kouider Makradi, Ahmed Hörsch, Martin Klein, Peter Hachemi, Mohamed El Preisig, Heinz A. Rezgui, Yacine Konchakova, Natalia Daouadji, Ali |
| author_facet | Peivaste, Iman Belouettar, Salim Mercuri, Francesco Fantuzzi, Nicholas Dehghani, Hamidreza Izadi, Razieh Ibrahim, Halliru Lengiewicz, Jakub Belouettar-Mathis, Maël Bendine, Kouider Makradi, Ahmed Hörsch, Martin Klein, Peter Hachemi, Mohamed El Preisig, Heinz A. Rezgui, Yacine Konchakova, Natalia Daouadji, Ali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12554 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie Peivaste, Iman Belouettar, Salim Mercuri, Francesco Fantuzzi, Nicholas Dehghani, Hamidreza Izadi, Razieh Ibrahim, Halliru Lengiewicz, Jakub Belouettar-Mathis, Maël Bendine, Kouider Makradi, Ahmed Hörsch, Martin Klein, Peter Hachemi, Mohamed El Preisig, Heinz A. Rezgui, Yacine Konchakova, Natalia Daouadji, Ali Materials Science Artificial Intelligence Computational Physics Quantum Physics 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. |
| title | Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie |
| topic | Materials Science Artificial Intelligence Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2601.12554 |