Artificial Intelligence in Materials Science and Engineering

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

This document provides a comprehensive review of the transformative impact of Artificial Intelligence (AI) on materials science and engineering (MSE). It highlights how AI, driven by algorithmic advancements and increased data availability, is becoming essential for accelerating discovery, optimizing material design, and navigating complexity. The review surveys a wide range of machine learning approaches, from traditional algorithms to advanced deep learning architectures like CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models such as Gaussian Processes for uncertainty quantification. A significant focus is placed on the pivotal role of data, detailing effective representation, featurization strategies (compositional, structural, image-based, language-inspired), and preprocessing for optimal model performance. The text addresses critical challenges including data quality, quantity, and standardization that profoundly impact model development and application in MSE. Key applications across the materials lifecycle are discussed, encompassing property prediction, high-throughput virtual screening, inverse design, process optimization, data extraction by large language models, and sustainability assessment. Finally, it explores critical challenges such as model interpretability, generalizability, and scalability, while outlining promising future directions, including hybrid physics-ML models, autonomous experimentation, and human-AI synergy.
#ArtificialIntelligence #MaterialsScience #MachineLearning #DeepLearning #MaterialsDesign #DataScience #PredictiveModeling

paper - https://arxiv.org/pdf/2601.12554v1
subscribe - https://t.me/arxivpaper
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