Machine learning models for easily obtainable descriptors of the electrocatalytic properties of Ag–Pd–Ir nanoalloys toward the formate oxidation reaction DOI
Xiaoqing Liu, Fuyi Chen,

Wanxuan Zhang

et al.

Nanoscale, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

By training the overpotential dataset of Ag–Pd–Ir nanocatalysts using machine learning models, untrained formate oxidation reaction catalyst is predicted K-nearest neighbors model, screening best candidate catalysts.

Language: Английский

A bibliographic analysis of optimization of hydrogen production via electrochemical method using machine learning DOI

Sadaf Iqbal,

Kiran Aftab,

Fakiha Tul Jannat

et al.

Fuel, Journal Year: 2024, Volume and Issue: 372, P. 132126 - 132126

Published: June 11, 2024

Language: Английский

Citations

15
Catalysis in the digital age: Unlocking the power of data with machine learning DOI Creative Commons
B. Moses Abraham, M. V. Jyothirmai, Priyanka Sinha

et al.

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2024, Volume and Issue: 14(5)

Published: Sept. 1, 2024

Abstract The design and discovery of new improved catalysts are driving forces for accelerating scientific technological innovations in the fields energy conversion, environmental remediation, chemical industry. Recently, use machine learning (ML) combination with experimental and/or theoretical data has emerged as a powerful tool identifying optimal various applications. This review focuses on how ML algorithms can be used computational catalysis materials science to gain deeper understanding relationships between properties their stability, activity, selectivity. development repositories, mining techniques, tools that navigate structural optimization problems highlighted, leading highly efficient sustainable future. Several data‐driven models commonly research diverse applications reaction prediction discussed. key challenges limitations using presented, which arise from catalyst's intrinsic complex nature. Finally, we conclude by summarizing potential future directions area ML‐guided catalyst development. article is categorized under: Structure Mechanism > Reaction Mechanisms Catalysis Data Science Artificial Intelligence/Machine Learning Electronic Theory Density Functional

Language: Английский

Citations

13
Metal-organic framework Cu-BTC for overall water splitting: A density functional theory study DOI
Huang Xu,

Kai-Yin Wu,

Chao Su

et al.

Chinese Chemical Letters, Journal Year: 2024, Volume and Issue: unknown, P. 109720 - 109720

Published: March 1, 2024

Language: Английский

Citations

10
Surface Phosphorus Grafting of Ti3C2Tx MXene as an Interface Charge “Bridge” for Efficient Electrocatalytic Hydrogen Evolution in All-pH Media DOI
Jian Zhang,

Xianzhi Yang,

Chen Chen

et al.

Chemistry of Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 4, 2024

The interface electronic structure of heterogeneous catalysts can be modulated by changing the surface coordination configuration, which is crucial to their catalytic activity. Herein, a phosphorus-grafted Ti3C2Tx MXene platform anchored with an MoS2 nanoflake heterojunction electrocatalyst was assembled through simple phosphorus-hydrothermal method. An charge "bridge" has been created grafting uniform P atoms coordinated O (P-Ti3C2Tx), resulting in charge-transfer channel between P-Ti3C2Tx and MoS2. Based on ultrafast transient absorption spectroscopy, fastest electron-transfer kinetics from (1.7 ps) slowest electron–hole recombination speed (28 were obtained over MoS2@P-Ti3C2Tx than those MoS2@O-Ti3C2Tx MoS2@OP-Ti3C2Tx. Benefiting lower carrier transport activation energy, exhibited stirring electrocatalytic activity toward hydrogen evolution all-pH media, delivered three low overpotentials 48.6, 63.2, 70.5 mV at 10 mA cm–2 alkaline, acid, neutral respectively. Grafting atomic scale create proposes new strategy design efficient pH-universal electrocatalyst.

Language: Английский

Citations

10
Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion DOI Creative Commons
Tianyi Wang, Qilong Wu, Yun Han

et al.

Applied Physics Reviews, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 6, 2025

Electrochemical reactions are pivotal for energy conversion and storage to achieve a carbon-neutral sustainable society, optimal electrocatalysts essential their industrial applications. Theoretical modeling methodologies, such as density functional theory (DFT) molecular dynamics (MD), efficiently assess electrochemical reaction mechanisms electrocatalyst performance at atomic levels. However, its intrinsic algorithm limitations high computational costs large-scale systems generate gaps between experimental observations calculation simulation, restricting the accuracy efficiency of design. Combining machine learning (ML) is promising strategy accelerate development electrocatalysts. The ML-DFT frameworks establish accurate property–structure–performance relations predict verify novel electrocatalysts' properties performance, providing deep understanding mechanisms. ML-based methods also solution MD DFT. Moreover, integrating ML experiment characterization techniques represents cutting-edge approach insights into structural, electronic, chemical changes under working conditions. This review will summarize DFT current application status design in various conversions. underlying physical fundaments, advancements, challenges be summarized. Finally, future research directions prospects proposed guide revolution.

Language: Английский

Citations

1
Mo4/3B2Tx induced hierarchical structure and rapid reaction dynamics in MoS2 anode for superior sodium storage DOI
Guilong Liu,

Wenzhuo Yuan,

Zihan Zhao

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 493, P. 152576 - 152576

Published: May 29, 2024

Language: Английский

Citations

8
Defect-modulated and heteroatom-functionalized Ti3-xC2Ty MXene 3D nanocavities induce growth of MoSe2 nanoflakes toward electrocatalytic hydrogen evolution in all pH electrolytes DOI
Mingliang Du,

Xianzhi Yang,

Jian Zhang

et al.

Nano Research, Journal Year: 2024, Volume and Issue: 17(7), P. 6294 - 6304

Published: April 19, 2024

Language: Английский

Citations

7
Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction DOI Creative Commons
Yue‐Fei Zhang, Xuefei Liu, Wentao Wang

et al.

ChemElectroChem, Journal Year: 2024, Volume and Issue: 11(13)

Published: April 11, 2024

Abstract Electrocatalytic hydrogen evolution reaction (HER) is a promising strategy to solve and mitigate the coming energy shortage global environmental pollution. Searching for efficient electrocatalysts HER remains challenging through traditional trial‐and‐error methods from numerous potential material candidates. Theoretical high throughput calculation assisted by machine learning possible method screen excellent effectively. This will pave way high‐efficiency low‐price electrocatalyst findings. In this review, we comprehensively introduce workflow standard models reduction reactions. mainly illustrates how used in catalyst filtration descriptor exploration. Subsequently, several applications, including surface electrocatalysts, two‐dimensional (2D) single/dual atom using electrocatalytic HER, are highlighted introduced. Finally, corresponding challenge perspective reactions concluded. We hope critical review can provide comprehensive understanding of design guide future theoretical experimental investigation

Language: Английский

Citations

5
Machine learning driven advancements in catalysis for predicting hydrogen evolution reaction activity DOI
Priyanka Sinha, M. V. Jyothirmai, B. Moses Abraham

et al.

Materials Chemistry and Physics, Journal Year: 2024, Volume and Issue: 326, P. 129805 - 129805

Published: Aug. 5, 2024

Language: Английский

Citations

5
Synthesis and optimization of Pt nanoparticle catalyst supported on heterogeneous composite Mo2C-N-C for acidic direct methanol fuel cell anodes DOI
Meihui Li, Qianhui Li, Xiaofeng Li

et al.

Applied Surface Science, Journal Year: 2024, Volume and Issue: 682, P. 161658 - 161658

Published: Nov. 1, 2024

Language: Английский

Citations

5