Impact statement

The convergence of AI and hydrogen energy represents one of the most significant technological intersections of this decade. AI-driven predictive maintenance using deep learning (DL) architectures such as long short-term memory (LSTM) and convolutional neural networks (CNNs) has demonstrated measurable benefits across three dimensions: reduced operational costs, minimized unplanned downtime and extended equipment lifespan. These gains are particularly significant in hydrogen production environments, where virtual sensors combined with AI have been shown to anticipate component failures before they occur, reducing unplanned downtime by 30–40%. These gains are not incremental; they are potentially transformative for making green hydrogen economically competitive. Beyond efficiency, AI-powered anomaly detection in hydrogen transport infrastructure has demonstrated a 70% reduction in potential safety incidents by identifying pressure deviations as small as 5% or temperature anomalies of 5 °C per minute. At the policy level, nations from China to Germany are already legislating AI-hydrogen integration into their national energy strategies, signaling growing international recognition that this convergence is not optional but necessary. For researchers, the imperative is to validate AI models at industrial scale. For policymakers, building regulatory frameworks that keep pace with deployment is essential, while industries must transition from fragmented, standalone AI applications to integrated, system-wide architectures. Given the urgency of climate change, safety imperatives and energy security, the stakes have never been higher.

Literature review

Research at the intersection of AI and hydrogen has grown exponentially, with China, the United States and India playing crucial roles in driving this advancement. This growth reflects both the urgency of the hydrogen transition and the maturation of AI tools capable of addressing its complexities. Franco (Reference Franco2025) maps this growing field across five key domains: electrolysis optimization, alternative production pathways, storage and transportation, end-use applications and cross-cutting policy activities. Each domain encompasses multiple active lines of investigation, reflecting the breadth and complexity of the hydrogen challenge.

The most extensively studied application is AI-driven optimization of hydrogen production. Electrolyzers, the heart of green hydrogen systems, must dynamically modulate in response to intermittent renewable energy inputs. AI optimization has proven highly effective across a range of electrochemical devices. Approaches such as neural networks and machine learning (ML) models have been applied to electrolyzers and fuel cells alike. The results are significant: better material prediction and far fewer costly experiments (Gupta et al., Reference Gupta, Srivastava and Khatri2026). Feng et al. (Reference Feng, Luo, Li, Pan, Tan and Chen2025) extended this by deploying digital twins coupled with ML to monitor gas crossover in alkaline electrolyzers, achieving continuous performance tracking without interrupting operations. More broadly, AI-driven systems have demonstrated up to 20% improvement in energy efficiency and 24% reduction in operational costs across electrolyzer platforms (Shash et al., Reference Shash, Abdeltawab, Hassan, Darweesh and Hegazy2025).

At the operational scheduling level, integrating AI-driven predictive simulation of electrolyzer operation with real-time electricity price forecasting has demonstrated the potential to reduce simulated green hydrogen production costs to approximately AUD 1.72/kg or ~ USD 1.19/kg (Butler and Kalam, Reference Butler and Kalam2025). This result is notable given that under the most favorable conditions, techno-economic modeling places the realistic minimum future production cost at USD 2.7/kg (Freire Ordóñez et al., Reference Freire Ordóñez, Ganzer, Halfdanarson, González Garay, Patrizio, Bardow, Guillén-Gosálbez, Shah and Mac Dowell2023). Current unsubsidized market prices remain 1.5–7 times higher than fossil-derived alternatives (IEA, 2025). These findings suggest meaningful potential for AI-driven optimization to contribute to cost reduction in green hydrogen production, though significant structural and economic barriers remain.

Predictive maintenance is another area of high maturity and high value. Abiola et al. (Reference Abiola, Andújar, Segura and Barragán2025) showed that virtual sensors combined with AI can anticipate component failures in hydrogen production plants before they occur, reducing unexpected downtime by 30–40%. DL architectures, particularly LSTM networks and CNNs, have proven especially effective for detecting early-stage degradation signatures in complex electromechanical systems, including electrolyzers and fuel cells (Abood et al., Reference Abood, Nasser and Al-Khazraji2022). These capabilities are directly relevant to safety: in hydrogen infrastructure, where a single undetected fault can trigger cascading failures, the ability to intervene before failure is not merely an efficiency gain but a safety imperative.

Digital twins have emerged as a unifying architecture for AI deployment in hydrogen systems. Sensor data, physics-based models and ML are increasingly being integrated to simulate electrolyzer operational states and forecast performance degradation (Sharma et al., Reference Sharma, Tyagi, Vijay, Singh, Upadhyayula, Srivastava and Pai2026). Feng et al. (Reference Feng, Luo, Li, Pan, Tan and Chen2025), for example, deployed digital twins coupled with ML to monitor gas crossover in alkaline electrolyzers, achieving continuous performance tracking without interrupting operations. Beyond production, An et al. (Reference An, Yang, Song, Hwang, Byun and Park2024) applied AI-enhanced digital twins to hydrogen refueling stations, demonstrating that anomalous pressure and temperature patterns could be detected with high sensitivity, enabling proactive safety interventions. These studies show how digital twins can connect equipment-level monitoring with system-level safety assessment. At the frontier of research, AI is also transforming hydrogen materials discovery. Meta’s Open Catalyst Project performed over 685 million AI simulations across more than 19,000 candidate catalyst materials, identifying hundreds of potential low-cost catalysts for green hydrogen production (Meta, Reference Meta2024). This represents a computational task wholly beyond the means of most laboratories. Google DeepMind’s GNoME model predicted 2.2 million new stable crystals using a graph neural network trained through active learning, expanding the number of technologically viable materials known to humanity by an unprecedented scale (Merchant and Cubuk, Reference Merchant and Cubuk2023). Microsoft’s MatterGen introduces a generative AI approach to materials design, directly generating novel materials conditioned on target properties such as mechanical, electronic and magnetic constraints, going beyond the limitations of conventional screening methods (Zeni et al., Reference Zeni, Pinsler, Zügner, Fowler, Horton, Tomioka and Xie2025). In storage, ML has been applied to screen over 900,000 metal–organic framework (MOF) structures across 19 databases to identify top-performing hydrogen storage candidates, a task completely impractical by conventional methods (Glasby and Moghadam, Reference Glasby and Moghadam2021). These advances signal a broader shift: AI is not merely optimizing existing hydrogen systems; it is actively redesigning them from the molecular level up.

Explainable AI (XAI) bridges algorithmic capability and operational trust. Agwu et al. (Reference Agwu, Alatefi and Alkouh2025) and Ahmed et al. (Reference Ahmed, Shehab, Elzeki, Darwish and Hassanein2024) demonstrated XAI frameworks using SHAP values to make hydrogen production predictions interpretable to plant operators. In safety-critical settings, this is essential. An AI system that cannot explain its recommendations will either be ignored or blindly followed. XAI integration is therefore both a technical and a governance priority.

Deployment challenges

Despite this momentum, the deployment of AI in hydrogen energy systems faces serious and underappreciated challenges. Lack of data and information fragmentation is the most pressing of these. Hydrogen facilities suffer from structural data poverty. Operational datasets for key equipment, such as electrolyzers, compressors, fuel cells and storage systems, are often proprietary, temporally limited or missing rare-event regimes. This data scarcity severely hinders the training of robust AI models (Coteț et al., Reference Coteț, Ferenci, Lakatos and Szabó2026). What data does exist is often fragmented across proprietary systems, with inconsistent formats between manufacturers and processes. This “data privatization” problem is not merely technical. It is structural, rooted in competitive dynamics that discourage sharing even when collective benefit is clear.”

A second challenge is the risk of AI hallucination and AI failure in high-stakes environments. Unlike in consumer applications, where a fabricated answer is merely inconvenient, an AI model that generates inaccurate pressure setpoints or misidentifies a fault signature in a hydrogen pipeline could have severe consequences. The lack of interpretability in DL models creates significant barriers to adoption in safety-critical infrastructure, where operators must be able to understand and trust automated decisions (Zhang and Strbac, Reference Zhang and Strbac2025). This is not a hypothetical concern but a present-day limitation that must be addressed through rigorous uncertainty quantification and validation protocols before AI systems are granted autonomous control authority.

The lab-to-factory gap presents a third barrier. AI models that perform impressively in simulation or on historical datasets frequently underperform when deployed in real industrial environments characterized by load fluctuations and material impurities. Long-term degradation also adds complexities that are absent from training data. The gap between a promising simulation and a working industrial system is wider than it appears. Kantaros et al. (Reference Kantaros, Ganetsos, Pallis and Papoutsidakis2025) highlight how real industrial deployments of model-based AI monitoring systems, including digital twins, routinely encounter data mismatches, latency issues and interoperability failures that lab environments simply do not expose. For hydrogen equipment that must run reliably for thousands of hours, these are not minor inconveniences but actual deployment blockers.

Perhaps the most urgent challenge, however, is regulatory. There are currently no unified international standards governing the validation, certification or liability assignment of AI systems deployed in hydrogen safety-critical infrastructure. The European Court of Auditors has cautioned that the EU’s hydrogen targets for 2030 may already be overly ambitious given current industrial capacities (HydroProject EU, 2025). Layering unvalidated AI systems into an already-stretched infrastructure without clear regulatory guardrails introduces systemic risk. The EU AI Act (2024) classifies AI systems used in critical infrastructure as high-risk, mandating conformity assessments and human oversight. However, hydrogen-specific technical standards under ISO TC/197 and IEC TC105 have yet to incorporate AI validation clauses (HydroProject EU, 2025). This regulatory lag is not a minor administrative gap; it is a structural vulnerability in the global hydrogen scale-up.

Finally, talent scarcity constrains deployment. The integration of AI into hydrogen systems demands professionals fluent in a rare combination of both electrochemistry and ML. A KPMG survey of over 1,000 companies found that only 56% of energy sector firms are even trying to adopt AI, compared to higher adoption rates in finance and retail (KPMG China and International Hydrogen Fuel Cell Association, 2025). Without deliberate investment in interdisciplinary training, the workforce gap will become a bottleneck as ambitions outpace capability.

Accelerating AI integration

Unlocking the full potential of AI in hydrogen energy requires action across multiple fronts simultaneously. On the technical side, the field must move from standalone AI applications to integrated architectures that optimize the full hydrogen value chain, from renewable energy input through electrolysis, storage and end-use. Frameworks that combine validated system-level models, multisource data fusion and hybrid AI processing, including supervised learning, reinforcement learning and physics-informed neural networks, with XAI-based output layers offer a promising blueprint for system-wide integration (Feng et al., Reference Feng, Luo, Li, Pan, Tan and Chen2025; Shash et al., Reference Shash, Abdeltawab, Hassan, Darweesh and Hegazy2025). Physics-informed neural networks are particularly valuable in this context as they embed thermodynamic and electrochemical constraints directly into model architecture, ensuring that AI recommendations remain physically consistent even when training data is scarce.

On the data side, the establishment of regional and international hydrogen data hubs, including shared repositories with standardized semantic formats and credibility labeling, would directly address the fragmentation problem. Federated learning offers a complementary pathway: enabling AI models to be trained collaboratively across facilities without exposing proprietary operational data. These are not distant aspirations; the technical infrastructure for both already exists and needs only coordinated policy commitment to deploy.

Policymakers and standards bodies must collaborate to develop AI-specific technical clauses within existing hydrogen safety standards. Collaboration between ISO TC/197, IEC TC105 and AI governance bodies such as the EU AI Office should be formalized, with clear timelines for producing certification frameworks applicable to AI systems in hydrogen infrastructure. Crucially, liability boundaries between AI technology suppliers and plant operators must be legally defined before, not after, incidents occur. Several national governments are already moving in this direction: Germany’s Hydrogen Acceleration Act (2024), Japan’s Hydrogen Society Promotion Act (2024) and China’s 2025 Implementation Opinions on AI in Energy, all signal a growing recognition that policy must actively shape and not merely react to this technological convergence.

Financial mechanisms also have a role to play. Government incentives, such as tax credits and infrastructure funding, have proven effective at catalyzing private investment in clean hydrogen. Expanding these mechanisms to cover AI integration costs, such as sensor networks, digital twin infrastructure and validation programs, can accelerate adoption, particularly for smaller operators who currently lack the capital to invest in AI systems independently.

Way forward

The central question posed in this editorial is whether we can trust AI in hydrogen environments where errors can be catastrophic. This question does not yet have a complete answer. What is clear is that the issues raised by this question must be taken more seriously. AI offers genuinely transformative capabilities for hydrogen safety and efficiency: predictive maintenance that anticipates failures before they occur, anomaly detection that identifies dangerous deviations in real time and model-based simulation tools that allow hazardous scenarios to be assessed without physical risk (Sina Finance, 2025). These are not marginal improvements. Collectively, AI integration is projected to reduce hydrogen system failure rates by 30%, cut energy consumption by 10% and increase production volumes by 20% by 2030 (KPMG China and International Hydrogen Fuel Cell Association, 2025).

However, capability without accountability is dangerous. The path forward requires a deliberate, phased approach to AI deployment. This begins with monitoring-only modes, progressing to decision support and only advancing to supervised automation once validation benchmarks are met and regulatory frameworks are in place. Research priorities should include uncertainty quantification methods that provide confidence bounds on all AI-driven decisions. XAI tools are also needed to make model reasoning legible to operators and regulators alike. Finally, industrial pilot programs must be established to generate the real-world data the field currently lacks.

Ultimately, the integration of AI into hydrogen energy systems is not merely a technical challenge but also a governance one. The nations and institutions that get this right will not only accelerate their own hydrogen transitions but also set the standards for others to follow. Responsible deployment of AI in hydrogen systems directly advances the United Nations’ Sustainable Development Goals (SDGs): SDG 7 (affordable and clean energy), SDG 9 (industry, innovation and infrastructure) and SDG 13 (climate action). It represents one of the most consequential opportunities available to the global energy community. Technology is advancing rapidly, and regulatory frameworks must now catch up.