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

TL;DR

This paper reviews how artificial intelligence is transforming materials science by enabling faster discovery, better design, and addressing key challenges like data quality and standardization, with insights into current methodologies and future directions.

Contribution

It provides a comprehensive overview of AI techniques, data strategies, and challenges in materials science, highlighting recent advancements and future research opportunities.

Findings

AI accelerates materials discovery and design.

Deep learning architectures like CNNs, GNNs, Transformers are increasingly used.

Data quality and standardization remain critical challenges.

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.