Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction

Molecules, Год журнала: 2025, Номер 30(4), С. 759 - 759

Опубликована: Фев. 7, 2025

Hydrogen as an environmentally friendly energy carrier, has many significant advantages, such cleanliness, recyclability, and high calorific value of combustion, which makes it one the major potential sources supply in future. evolution reaction (HER) is important strategy to cope with global shortage environmental degradation, given large cost involved HER, crucial screen develop stable efficient catalysts. Compared traditional catalyst development model, rapid data science technology, especially machine learning shown great field recent years. Among them, research method combining high-throughput computing received extensive attention materials science. Therefore, this paper provides a review on guide HER electrocatalysts, covering application constructing prediction models extracting key features catalytic activity. The future challenges directions are also prospected, aiming provide useful references lessons for related research.

Язык: Английский

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Machine learning in electrocatalysis - living up to the hype?

Current Opinion in Electrochemistry, Год журнала: 2025, Номер unknown, С. 101649 - 101649

Опубликована: Янв. 1, 2025

Язык: Английский

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Quantum Machine Learning: A Comprehensive Review of Integrating AI with Quantum Computing for Computational Advancements

MethodsX, Год журнала: 2025, Номер 14, С. 103318 - 103318

Опубликована: Апрель 18, 2025

Quantum Machine Learning (QML) is the emerging confluence of quantum computing and artificial intelligence that promises to solve computational problems inaccessible classical systems. Using principles such as superposition, entanglement, interference, QML exponential speed-ups new paradigms for data processing in machine learning tasks. This review gives an overview QML, from advancements quantum-enhanced ML native algorithms hybrid quantum-classical frameworks. It varies applications optimization, drug discovery, quantum-secured communications, showcasing how can change healthcare, finance, logistics industries. Even though this approach holds so much promise, significant challenges remain be addressed-noisy qubits, error correction, limitations encoding-that must overcome by interdisciplinary research soon. The paper tries collate state art theoretical underpinnings, practical applications, directions into future.

Язык: Английский

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Machine learning‐accelerated computational screening of CrNiCu ternary alloy as superior cocatalyst for photocatalytic hydrogen evolution

Materials Genome Engineering Advances, Год журнала: 2025, Номер unknown

Опубликована: Май 12, 2025

Abstract The development of cost‐effective noble‐metal‐free cocatalysts with exceptional hydrogen evolution reaction (HER) activity is critical for advancing scalable and sustainable photocatalytic production. Although platinum (Pt) remains a benchmark HER catalyst, its scarcity high cost stimulates the search viable alternatives. In this work, machine learning (ML)‐accelerated strategy presented to screen highly active ternary CrNiCu alloys. Combining density functional theory calculations, XGBoost regression models were trained predict adsorption energies water dissociation energy barriers on alloy surfaces. Consequently, theoretical exchange current densities predicted all possible compositions alloys, enabling identification catalysts composition 10∼30 at.% Cr, 30–50 Ni, 20–60 Cu that exhibits superior than Pt. Stability assessment optimal alloys further confirms their excellent resistance element segregation hydroxyl poisoning under operational conditions. This work not only identifies promising non‐noble but also establishes an efficient ML‐accelerated computational framework discovery durable high‐activity renewable applications.

Язык: Английский

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Quaternary Transition Metal Dichalcogenides (M1-xNxX2(1-y)Y2y) for Hydrogen Evolution: A Review on Atomic Structure, 3D Engineering, and Electrocatalytic Performance

Progress in Solid State Chemistry, Год журнала: 2025, Номер unknown, С. 100532 - 100532

Опубликована: Май 1, 2025

Язык: Английский

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Machine learning-assisted design of transition metal-doped 2D WSn₂N₄ electrocatalysts for enhanced hydrogen evolution reaction

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 90, С. 599 - 606

Опубликована: Окт. 8, 2024

Язык: Английский

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