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Description
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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 applications are discussed across the materials lifecycle, including property prediction at multiple scales, high-throughput virtual screening, inverse design, process optimization, data extraction by large language models, and sustainability assessment. Critical challenges such as model interpretability, generalizability, and scalability are addressed, alongside promising future directions involving hybrid physics-ML models, autonomous experimentation, collaborative platforms, and human-AI synergy. (2025-11-15)
***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to https://doi.org/10.1016/j.compstruct.2025.119419 for the original and latest version of the dataset and data downloads*** (2026-06-09)
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Keyword
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Autonomous experimentation, Convolutional neural networks (CNNs), Data integration, deep learning, Digital product passport, Featurization, Graph neural networks (GNNs), Lifecycle assessment, machine learning, Materials design, Materials discovery, Materials modeling, Neural networks, Predictive modeling, Process optimization, Property prediction, Standardization, Supervised learning, sustainability, Unsupervised learning |
