Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Nano-Micro Letters, Journal Year: 2023, Volume and Issue: 15(1)

Published: Oct. 13, 2023

Abstract Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method producing advanced is not only cost-ineffective but also time-consuming labor-intensive. Fortunately, advancement of machine learning brings new opportunities discovery design. By analyzing experimental theoretical data, can effectively predict their evolution reaction (HER) performance. This review summarizes recent developments in low-dimensional electrocatalysts, including zero-dimension nanoparticles nanoclusters, one-dimensional nanotubes nanowires, two-dimensional nanosheets, as well other electrocatalysts. In particular, effects descriptors algorithms on screening investigating HER performance highlighted. Finally, future directions perspectives electrocatalysis discussed, emphasizing potential to accelerate electrocatalyst discovery, optimize performance, provide insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding current state its research.

Language: Английский

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When Machine Learning Meets 2D Materials: A Review

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

Published: Jan. 26, 2024

Abstract The availability of an ever‐expanding portfolio 2D materials with rich internal degrees freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together the unique ability to tailor heterostructures made by in a precisely chosen stacking sequence relative crystallographic alignments, offers unprecedented platform for realizing design. However, breadth multi‐dimensional parameter space massive data sets involved is emblematic complex, resource‐intensive experimentation, which not only challenges current state art but also renders exhaustive sampling untenable. To this end, machine learning, very powerful data‐driven approach subset artificial intelligence, potential game‐changer, enabling cheaper – yet more efficient alternative traditional computational strategies. It new paradigm autonomous experimentation accelerated discovery machine‐assisted design functional heterostructures. Here, study reviews recent progress such endeavors, highlight various emerging opportunities frontier research area.

Language: Английский

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From prediction to design: Recent advances in machine learning for the study of 2D materials

Nano Energy, Journal Year: 2023, Volume and Issue: 118, P. 108965 - 108965

Published: Oct. 4, 2023

Language: Английский

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Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

This review explores machine learning's impact on designing electrocatalysts for hydrogen energy, detailing how it transcends traditional methods by utilizing experimental and computational data to enhance electrocatalyst efficiency discovery.

Language: Английский

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Hydrogen evolution descriptors: A review for electrocatalyst development and optimization

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 59, P. 30 - 42

Published: Feb. 5, 2024

Language: Английский

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A machine learning framework for accelerating the development of highly efficient methanol synthesis catalysts

Journal of Energy Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Pyrrole-type TM-N3 sites as high-efficient bifunctional oxygen reactions electrocatalysts: From theoretical prediction to experimental validation

Journal of Energy Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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The rational co-doping strategy of transition metal and non-metal atoms on g-CN for highly efficient hydrogen evolution by DFT and machine learning

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 56, P. 949 - 958

Published: Jan. 1, 2024

Language: Английский

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Advancing CO₂RR with O-Coordinated Single-Atom Nanozymes: A DFT and Machine Learning Exploration

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(18), P. 14021 - 14030

Published: Sept. 9, 2024

Language: Английский

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Urea Electrosynthesis Accelerated by Theoretical Simulations

Advanced Functional Materials, Journal Year: 2023, Volume and Issue: 34(14)

Published: Dec. 27, 2023

Abstract Urea is not only a primary fertilizer in modern agriculture but also crucial raw material for the chemical industry. In past hundred years, prevailing industrial synthesis of urea heavily relies on Bosch–Meiser process to couple NH 3 and CO 2 under harsh conditions, resulting high carbon emissions energy consumption. The conversion carbon‐ nitrogen‐containing species into through electrochemical reactions ambient conditions represents sustainable strategy. Despite increasing reports electrosynthesis, comprehensive review that delves profound, atomic‐level comprehension fundamental reaction mechanisms currently absent. this Perspective, recent advancements from /CO various nitrogenous (i.e., N , NO x − NO) are presented, with special emphasis theoretical understanding C─N coupling mechanisms. Several key strategies facilitate then pinpointed, which enhance their applicability practical experiments highlight significant progress achieved field. At end, major obstacles potential opportunities advancing electrosynthesis accelerated by simulations situ techniques discussed. This hoped act as roadmap ignite fresh insights inspiration development electrocatalytic synthesis.

Language: Английский

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