Machine learning descriptor-assisted exploration of metal-modified graphene hydrogen storage materials

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 119, С. 45 - 55

Опубликована: Март 20, 2025

Язык: Английский

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Modulation of Hydrogen Evolution Reaction Performance of MXenes by Doped Transition Metals: Comprehensive Exploration of High-Throughput Computing and Machine Learning

ACS Applied Materials & Interfaces, Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

Due to the unique properties of MXenes, doping transition metals can modulate their catalytic and make them potential materials for hydrogen evolution reaction (HER). Nevertheless, extensive combinatorial space poses a challenge rapid screening catalysts. To address this issue, we conducted high-throughput calculations on series metal atom-doped Ti3CNO2 Zr2HfCNO2. Furthermore, local structure corresponding electronic changes are analyzed, focusing influence HER properties. site identification features were introduced train multisite prediction model with final accuracy R2 = 0.97 predicted trend adsorption Gibbs free energy (ΔGH*) across range MXenes structures, which doped TM atoms. The results show that Nb, Sc, Rh, W, Ti, V resulted in |ΔGH*| < 0.2 eV more than 38 M'2M″CNO2, respectively, they effective dopant atoms enhancing ability M'2M″CNO2. This study not only demonstrates performance but also highlights importance models development efficient

Язык: Английский

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Guided electrocatalyst design through in-situ techniques and data mining approaches

Nano Convergence, Год журнала: 2025, Номер 12(1)

Опубликована: Апрель 18, 2025

Abstract Intuitive design strategies, primarily based on literature research and trial-and-error efforts, have significantly contributed to advancements in the electrocatalyst field. However, inherently time-consuming inconsistent nature of these methods presents substantial challenges accelerating discovery high-performance electrocatalysts. To this end, guided approaches, including in-situ experimental techniques data mining, emerged as powerful catalyst optimization tools. The former offers valuable insights into reaction mechanisms, while latter identifies patterns within large databases. In review, we first present examples using techniques, emphasizing a detailed analysis their strengths limitations. Then, explore data-mining-driven development, highlighting how data-driven approaches complement accelerate catalysts. Finally, discuss current possible solutions for design. This review aims provide comprehensive understanding methodologies inspire future innovations electrocatalytic research.

Язык: Английский

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Valence electron matching law for MXene-based single-atom catalysts

Journal of Energy Chemistry, Год журнала: 2024, Номер unknown

Опубликована: Окт. 1, 2024

Язык: Английский

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