Machine Learning-Driven Advances in Hydrogen Embrittlement of Steels: A Comprehensive Review

Abstract

Hydrogen embrittlement (HE) remains one of the key challenges limiting the safe and reliable deployment of steels in hydrogen production, storage, transportation, and utilization systems. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for predicting HE behavior, accelerating materials development and selection, while supporting engineering decision-making. This paper systematically reviews and critically evaluates AI/ML applications for HE in steels through a structured analysis of all studies published between 2010 and 2026. The review examines experimental, literature-derived, and computational datasets together with data preprocessing, feature engineering, AI/ML models, validation strategies, and prediction objectives. Experimental datasets remain the dominant source for predicting HE susceptibility, hydrogen concentration, fracture behavior, and hydrogen-assisted cracking, whereas computational datasets provide complementary mechanistic insights into hydrogen diffusion, trapping, crack propagation, and atomistic interactions across multiple scales. Image- and signal-based modalities within these datasets further enable computer vision and automated defect characterization. Beyond systematically synthesizing the current literature, this review provides a critical and analytical evaluation of AI/ML datasets, model families, and prediction applications. It also establishes a practical framework for selecting appropriate AI/ML approaches according to dataset characteristics and engineering objectives. Future research should focus on standardized HE databases, rigorous external validation, explainable and uncertainty-aware AI, and closer integration of data-driven and physics-based approaches to improve predictive reliability, mechanistic understanding, and the safe deployment of hydrogen-compatible steels.