Hydrogen embrittlement remains a critical challenge in the deployment of hydrogen infrastructure, particularly in pipelines and storage tanks. The phenomenon, which leads to material degradation and potential failure under stress, is influenced by complex interactions between hydrogen, material microstructure, and mechanical loads. Traditional experimental methods for assessing embrittlement are time-consuming and costly, prompting the adoption of artificial intelligence (AI) to accelerate predictions and optimize material selection. AI models leverage material science data, stress simulations, and environmental conditions to forecast embrittlement risks with high accuracy.

One prominent approach involves machine learning (ML) models trained on datasets combining material properties, hydrogen exposure conditions, and mechanical performance metrics. For instance, neural networks have been applied to predict the susceptibility of high-strength steels used in hydrogen pipelines. These models analyze variables such as grain size, dislocation density, and hydrogen diffusion rates to estimate crack initiation probabilities. In the aerospace sector, where lightweight alloys are exposed to hydrogen, AI has been used to evaluate titanium and aluminum alloys for hydrogen compatibility. By processing microstructural data from electron microscopy and mechanical test results, ML algorithms identify embrittlement-prone regions in fuel tanks and propulsion systems.

Another advanced technique integrates physics-informed neural networks (PINNs) with finite element analysis (FEA) simulations. PINNs incorporate fundamental equations of hydrogen diffusion and stress-strain relationships into their architecture, ensuring predictions align with physical laws. This hybrid approach has been tested on carbon steel pipelines, where it accurately predicted hydrogen-induced crack growth under cyclic loading. The energy sector has adopted similar models for underground hydrogen storage in salt caverns, where pressure fluctuations and geomechanical stresses pose embrittlement risks. AI-driven simulations help operators adjust pressure limits and material coatings to mitigate damage.

Support vector machines (SVMs) and random forest algorithms have also shown promise in classifying materials based on embrittlement resistance. These models process datasets from standardized tests like slow strain rate testing (SSRT) and thermal desorption spectroscopy (TDS) to rank materials for specific applications. For example, in the European natural gas grid repurposing project, SVMs were employed to assess pipeline steels for hydrogen service. The models considered alloy composition, heat treatment history, and operational pressure to recommend safe hydrogen blending ratios.

In the energy sector, AI has been applied to optimize storage tank designs for liquid hydrogen. Deep learning models trained on cryogenic fracture mechanics data predict how welded joints in stainless steel tanks respond to thermal and mechanical stresses. Aerospace applications, such as liquid hydrogen storage for rockets, have benefited from these models by identifying optimal welding parameters and post-weld heat treatments to minimize embrittlement.

Emerging techniques include generative adversarial networks (GANs) for synthetic data generation, which expand training datasets where experimental data is scarce. GANs create realistic microstructural images and mechanical property tables, enabling ML models to generalize across untested materials. This approach has been piloted in research on nickel-based superalloys for hydrogen turbines, where data scarcity limits traditional ML performance.

Despite these advancements, challenges remain in standardizing input data formats and validating AI predictions against real-world performance. Variability in material batches and environmental conditions necessitates continuous model updates via transfer learning. Collaborative efforts between academia and industry aim to establish benchmark datasets and testing protocols for AI-driven embrittlement prediction.

The integration of AI into hydrogen infrastructure planning is already yielding tangible benefits. Energy companies use predictive models to schedule maintenance and replace high-risk components before failures occur. Aerospace manufacturers leverage AI to qualify materials for next-generation hydrogen-powered aircraft, reducing certification timelines. As AI techniques evolve, their role in ensuring the safety and longevity of hydrogen systems will expand, supporting the transition to a hydrogen-based energy economy.

The following table summarizes key AI models and their applications in hydrogen embrittlement prediction:

Model Type | Application Example | Data Inputs
-------------------------|---------------------------------------------|-----------------------------
Neural Networks | High-strength steel pipelines | Grain size, diffusion rates
Physics-Informed NN | Carbon steel pipeline crack growth | FEA simulations, SSRT data
Support Vector Machines | Natural gas grid repurposing | Alloy composition, pressure
Random Forests | Liquid hydrogen storage tanks | Cryogenic fracture data
Generative Adversarial N | Nickel-based superalloys | Synthetic microstructures

The ongoing refinement of these models will enhance their predictive accuracy and broaden their applicability across industries. By combining material science insights with AI-driven analytics, researchers and engineers can address hydrogen embrittlement more effectively, ensuring the safe scaling of hydrogen technologies.