水分解用電極触媒設計への機械学習アプローチ：グリーン水素製造に向けたレビュー
Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production (原題)
Vamsi Krishna Kudapa, Shoaib Mohd, Vijayakumar Sivasundar, Akanksha Mishra, Santosh Kumar Sahu, Diaa S. Metwally, Mohammed Aman
🤖 gxceed AI 要約
日本語
本レビューは、水電解によるグリーン水素製造のボトルネックである電極触媒開発に対し、機械学習を適用する研究ライフサイクルを体系的に整理する。データ源・特徴量化・アルゴリズムから、合金や単原子触媒のハイスループット仮想スクリーニング、安定性最適化、自己駆動型実験室までを概観する。データ不足や計算予測と産業実装の乖離といった課題も論じ、閉ループ探索の将来方向を示す。
English
This review systematically maps how machine learning accelerates electrocatalyst R&D for green hydrogen via water splitting. It covers data sources, featurization, and algorithms—from interpretable models to graph neural networks and generative models—for high-throughput screening of alloys and single-atom catalysts, multifunctional activity prediction, and stability optimization. It also addresses data scarcity, interpretability, and the gap between computational predictions and industrial deployment, pointing toward closed-loop discovery and self-driving labs.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は水素基本戦略やグリーン成長戦略で水電解・グリーン水素を重点領域とし、電極触媒の素材開発競争力は産業政策上重要。MLによる探索加速は国内の水素サプライチェーン構築やコスト低減に直結しうる。
In the global GX context
Globally, green hydrogen is central to net-zero pathways and transition finance, with electrocatalyst cost and efficiency as key bottlenecks. This review connects AI-driven materials discovery to the broader decarbonization agenda, relevant to ISSB/TCFD-aligned transition planning and industrial decarbonization investment.
👥 読者別の含意
🔬研究者:MLと電気化学の融合による触媒探索の最新手法と課題を俯瞰でき、研究設計の指針となる。
🏢実務担当者:水素事業や素材メーカーが、触媒開発のスピードアップとコスト低減に向けたML活用の可能性を評価する材料になる。
🏛政策担当者:水素戦略やグリーン成長戦略における素材開発支援の方向性を検討する際の技術的根拠を提供する。
📄 Abstract（原文）
The production of green hydrogen through water splitting requires highly efficient electrocatalysts, but the current trial-and-error-based synthesis or discovery is time-consuming, costly and resource-intensive. Machine learning (ML) provides a powerful, data-driven alternative that can model complex structure-activity relationships across large chemical spaces at orders-of-magnitude speed. This review systematically overviews the life cycle of the electrocatalyst research and development application of ML. First, the thermodynamic and kinetic principles of the hydrogen and oxygen evolution reactions are summarised, along with some well-adopted and accepted activity descriptors. Then we explore data sources, featurization approaches, and algorithms, and discuss the model space, from a simple interpretable model to a graph neural network to a generative model, in the context of the ML toolkit. Strategic applications are discussed for high-throughput virtual screening of alloys and single-atom catalysts, as well as multifunctional activity prediction for overall water splitting, and stability optimisation under operating conditions. The topic of emerging frontiers is highlighted, including high-entropy alloys, amorphous materials, and linking atomic-scale understanding to device-level performance through integration with density functional theory. Finally, the problems of data scarcity, model interpretability and the discrepancy between computational predictions and industrial implementation are discussed, along with future directions for closed-loop discovery and self-driving laboratories. Incorporating ML into electrocatalyst design and combining it with autonomous experimentation will revolutionise this process, from simulation to energy solution, dramatically speeding it up.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2026.1894425/pdffirst seen 2026-09-21 05:04:53
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