mlsanityData to 7 Oct 2026

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

arXiv 2601.12554 ↗First posted 18 Jan 2026cond-mat.mtrl-sci cs.AI physics.comp-phcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 78 of its 486 references that are in the map. First posted on arXiv 18 Jan 2026.

Builds on

78 of 486 references.

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50 most cited of 78 All 78 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026ChemNavigator: Agentic AI Discovery of Design Rules for Organic Photocatalystscited by 2
  2. 2026Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworkscited by 1
  3. 2026From Phase Prediction to Phase Design: A ReAct Agent Framework for High-Entropy Alloy Discoverycited by 0
  4. 2026Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioningcited by 0
  5. 2026MatMMExtract: An Open-Source Pipeline for Panel-Level Extraction of Grounded Image-Text Pairs from Materials Science Literaturecited by 0