Harnessing the Cobalt-Catalyzed Hydrogen Evolution Reaction through a Data-Driven Approach DOI
Guangchao Liang, Min Zhang

Inorganic Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

The design of cobalt complexes for the hydrogen evolution reaction (HER) has garnered significant attention over past few decades. To address limitations traditional trial-and-error method, we introduced strategy a simplified mechanism-based approach with data-driven practice (SMADP) in this study. Our results indicate that polypyridyl DPA-Bpy family (DPA-Bpy = N,N-bis(2-pyridinylmethyl)-2,2′-bipyridine-6-methanamine) generally follow electron transfer (E)–chemical proton (C)–electron (C) pathway HER. However, involvement proton-coupled (PCET) formation [CoII(L)–H]+ intermediate been observed PY5Me2 (PY5Me2 2,6-bis(1,1-di(pyridin-2-yl)ethyl)pyridine). Furthermore, hydricity (ΔGH–) and CoIII–H/CoII–H reduction potential (ERed°) are found to be active descriptors cobalt-catalyzed Excellent two-parameter regression models (ΔGH– ERed°) H2 molecule have obtained (R2 0.9429 R2 0.9854 family). demonstrate SMADP is groundbreaking method delineating This could also accelerate novel enhanced

Language: Английский

Computational chemistry for water-splitting electrocatalysis DOI
Licheng Miao, Wenqi Jia, Xuejie Cao

et al.

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: 53(6), P. 2771 - 2807

Published: Jan. 1, 2024

This review presents the basics of electrochemical water electrolysis, discusses progress in computational methods, models, and descriptors, evaluates remaining challenges this field.

Language: Английский

Citations

63
Activity and Selectivity Roadmap for C–N Electro-Coupling on MXenes DOI
Yiran Jiao, Haobo Li, Yan Jiao

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(28), P. 15572 - 15580

Published: July 6, 2023

Electrochemical coupling between carbon and nitrogen species to generate high-value C-N products, including urea, presents significant economic environmental potentials for addressing the energy crisis. However, this electrocatalysis process still suffers from limited mechanism understanding due complex reaction networks, which restricts development of electrocatalysts beyond trial-and-error practices. In work, we aim improve mechanism. This goal was achieved by constructing activity selectivity landscape on 54 MXene surfaces density functional theory (DFT) calculations. Our results show that step is largely determined *CO adsorption strength (Ead-CO), while relies more co-adsorption *N (Ead-CO Ead-N). Based these findings, propose an ideal catalyst should satisfy moderate stable adsorption. Through machine learning-based approach, data-driven formulas describing relationship Ead-CO Ead-N with atomic physical chemistry features were further identified. identified formula, 162 materials screened without time-consuming DFT Several potential catalysts predicted good performance, such as Ta2W2C3. The candidate then verified study has incorporated learning methods first time provide efficient high-throughput screening method selective electrocatalysts, could be extended a wider range electrocatalytic reactions facilitate green chemical production.

Language: Английский

Citations

61
Machine Learning Descriptors for Data‐Driven Catalysis Study DOI Creative Commons

Li‐Hui Mou,

TianTian Han,

Pieter E. S. Smith

et al.

Advanced Science, Journal Year: 2023, Volume and Issue: 10(22)

Published: May 16, 2023

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful predictive abilities. The selection of appropriate input features (descriptors) plays decisive role in improving the accuracy ML models uncovering key factors that influence activity selectivity. This review introduces tactics utilization extraction descriptors ML-assisted experimental research. In addition effectiveness advantages various descriptors, their limitations are also discussed. Highlighted both 1) newly developed spectral performance prediction 2) novel paradigm combining computational through suitable intermediate descriptors. Current challenges future perspectives on application techniques presented.

Language: Английский

Citations

56
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design DOI
Jorge Benavides-Hernández, Franck Dumeignil

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(15), P. 11749 - 11779

Published: July 24, 2024

This review paper delves into synergistic integration of artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) in the field heterogeneous catalysis, presenting a broad spectrum contemporary methodologies innovations. We methodically segmented text three core areas: catalyst characterization, data-driven exploitation, discovery. In characterization part, we outline current prospective techniques used for HTE how AI-driven strategies can streamline or automate their analysis. The exploitation part is divided themes, strategies, that offer flexibility either modular application creation customized solutions. exploration present applications enable areas outside experimentally tested chemical space, incorporating section on computational methods identifying new prospects. concludes by addressing limitations within suggesting possible avenues future research.

Language: Английский

Citations

27
Progress and challenges in nitrous oxide decomposition and valorization DOI
Xuanhao Wu, Jiaxin Du, Yanxia Gao

et al.

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: 53(16), P. 8379 - 8423

Published: Jan. 1, 2024

Nitrous oxide (N

Language: Английский

Citations

25
Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis DOI
Christoph Scheurer, Karsten Reuter

Nature Catalysis, Journal Year: 2025, Volume and Issue: 8(1), P. 13 - 19

Published: Jan. 29, 2025

Language: Английский

Citations

4
Advanced 3D ordered electrodes for PEMFC applications: From structural features and fabrication methods to the controllable design of catalyst layers DOI Creative Commons
Kaili Wang, Tingting Zhou, Zhen Cao

et al.

Green Energy & Environment, Journal Year: 2023, Volume and Issue: 9(9), P. 1336 - 1365

Published: Nov. 7, 2023

The catalyst layers (CLs) electrode is the key component of membrane assembly (MEA) in proton exchange fuel cells (PEMFCs). Conventional electrodes for PEMFCs are composed carbon-supported, ionomer, and Pt nanoparticles, all immersed together sprayed with a micron-level thickness CLs. They have performance trade-off where increasing loading leads to higher abundant triple-phase boundary areas but increases cost. Major challenges must be overcome before realizing its wide commercialization. Literature research revealed that it impossible achieve durability targets only high-performance catalysts, so controllable design CLs architecture MEAs now top priority meet industry goals. From this perspective, 3D ordered circumvents issue support-free ultrathin while reducing noble metal loadings. Herein, we discuss motivation in-depth summarize necessary structural features designing ultralow electrodes. Critical issues remain progress studied characterized. Furthermore, approaches development, involving material design, structure optimization, preparation technology, characterization techniques, summarized expected next-generation PEMFCs. Finally, review concludes perspectives on possible directions CL address significant future.

Language: Английский

Citations

24
Automation and machine learning augmented by large language models in a catalysis study DOI Creative Commons
Yuming Su, Xue Wang,

Yuanxiang Ye

et al.

Chemical Science, Journal Year: 2024, Volume and Issue: 15(31), P. 12200 - 12233

Published: Jan. 1, 2024

AI and automation are revolutionizing catalyst discovery, shifting from manual methods to high-throughput digital approaches, enhanced by large language models.

Language: Английский

Citations

17
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
High throughput screening for electrocatalysts for nitrogen reduction reaction using metal-doped bilayer borophene: A combined approach of DFT and machine learning DOI
Chen Chen, Bo Xiao, Zhongwei Li

et al.

Molecular Catalysis, Journal Year: 2024, Volume and Issue: 557, P. 113972 - 113972

Published: Feb. 28, 2024

Language: Английский

Citations

12