The Synergistic Effect between Metal and Sulfur Vacancy to Boost CO₂ Reduction Efficiency: A Study on Descriptor Transferability and Activity Prediction

JACS Au, Journal Year: 2024, Volume and Issue: 4(1), P. 125 - 138

Published: Jan. 10, 2024

Both metal center active sites and vacancies can influence the catalytic activity of a catalyst. A quantitative model to describe synergistic effect between centers is highly desired. Herein, we proposed machine learning evaluate index, PSyn, which learned from possible pathways for CH4 production CO2 reduction reaction (CO2RR) on 26 metal-anchored MoS2 with without sulfur vacancy. The data set consists 1556 intermediate structures MoS2, are used training. 2028 literature, comprising both single site dual sites, external test. XGBoost 3 features, including electronegativity, d-shell valence electrons metal, distance vacancy, exhibited satisfactory prediction accuracy limiting potential. Fe@Sv-MoS2 Os@MoS2 predicted be promising CO2RR catalysts high stability, low potential, selectivity against hydrogen evolution reactions (HER). Based some easily accessible descriptors, transferability achieved porous materials 2D in predicting energy change nitrogen (NRR). Such predictive also applied predict other oxygen tungsten vacancy systems.

Language: Английский

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Automated Machine Learning of Interfacial Interaction Descriptors and Energies in Metal-Catalyzed N₂ and CO₂ Reduction Reactions

Langmuir, Journal Year: 2025, Volume and Issue: 41(5), P. 3490 - 3502

Published: Jan. 31, 2025

The applications of machine learning (ML) in complex interfacial interactions are hindered by the time-consuming process manual feature selection and model construction. An automated ML program was implemented with four subsequent steps: data distribution analysis, dimensionality reduction clustering, selection, optimization. Without need intervention, descriptors metal charge variance (ΔQCT) electronegativity substrate (χsub) (δχM) were raised up good performance predicting electrochemical reaction energies for both nitrogen (NRR) CO2 (CO2RR) on metal-zeolites MoS2 surfaces. important role tuning catalytic reactivity NRR CO2RR highlighted from SHAP analysis. It proposed that Fe-, Cr-, Zn-, Nb-, Ta-zeolites favorable catalysts NRR, while Ni-zeolite showed a preference CO2RR. elongated bond N2 or bent configuration shown V-, Co-, Mo-zeolites, indicating molecule could be activated after adsorption pathways. generalizability automatically built is demonstrated to other systems such as metal-organic frameworks SiO2 useful tool accelerate data-driven exploration relationship between structures material properties without selection.

Language: Английский

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Transformative strategies in photocatalyst design: merging computational methods and deep learning

Journal of Materials Informatics, Journal Year: 2024, Volume and Issue: 4(4)

Published: Dec. 31, 2024

Photocatalysis is a unique technology that harnesses solar energy through in-situ processes, operating without the need for external inputs. It integral to advancing environmental, energy, chemical, and carbon-neutral objectives, promoting dual goals of pollution control carbon reduction. However, conventional approach photocatalyst design faces challenges such as inefficiency, high costs, low success rates, highlighting integrating modern technologies seeking new paradigms. Here, we demonstrate comprehensive overview transformative strategies in design, combining computational materials science with deep learning technologies. The review covers fundamental principles followed by examination methods workflow deep-learning-assisted design. Deep approaches are extensively reviewed, focusing on discovery novel photocatalysts, microstructure property optimization, approaches, application exploration, mechanistic insights into photocatalysis. Finally, highlight synergy between multidimensional computation learning, while discussing future directions development. This offers summary offering not only enhance development photocatalytic but also expand practical applications photocatalysis various domains.

Language: Английский

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AI-empowered digital design of zeolites: Progress, challenges, and perspectives

APL Materials, Journal Year: 2025, Volume and Issue: 13(2)

Published: Feb. 1, 2025

The rise of artificial intelligence (AI) as a powerful research tool in materials science has been extensively acknowledged. Particularly, exploring zeolites with target properties is vital significance for industrial applications, integrating AI technologies into zeolite design undoubtedly brings immense promise the advancements this field. Here, we provide comprehensive review AI-empowered digital zeolites. It showcases state-of-the-art progress predicting zeolite-related properties, employing machine learning potentials simulations, using generative models inverse design, and aiding experimental synthesis challenges perspectives are also discussed, emphasizing new opportunities at intersection This expected to offer crucial guidance advancing innovations through future.

Language: Английский

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Computational design of spatially confined triatomic catalysts for nitrogen reduction reaction

Journal of Materials Informatics, Journal Year: 2023, Volume and Issue: 3(4)

Published: Dec. 21, 2023

The electrocatalytic process of nitrogen reduction reactions (NRR) offers a promising approach towards achieving sustainable ammonia production, acting as an environmentally friendly replacement for the conventional Haber-Bosch method. Density functional theory calculations have been utilized to design and investigate set catalysts known triple-atom (TACs) electrochemical NRR, which are supported on graphite-C3N3 nanosheets. Herein, we systematically evaluated these TACs using stringent screening assess their catalytic performance. Among candidates, Pt3, Re3, Ru3 trimers emerged highly active with decent selectivity, involving limiting potential range -0.35~-0.11 V. According analysis electronic properties, determined that high NRR activity stems from d -π* electron-accepting -donating mechanism. Significantly, correlation between chemical structure was established pivotal physical parameter, has led conclusion can precisely control behavior transition metal trimer clusters by selecting appropriate elements designing moderate cluster-substrates interactions. In summary, theoretical studies not only enhance our understanding how properties governed metal-support interactions, regulating stability, activity, but also offer useful method novel NRR.

Language: Английский

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Conversion of biomass combustion ash to zeolites by low-temperature alkali fusion method

Next research., Journal Year: 2025, Volume and Issue: unknown, P. 100359 - 100359

Published: April 1, 2025

Language: Английский

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Mechanism of fluorescent quenching in ship fuel nitrogen content detection using Nb-doped SnO2 quantum dots as the fluorescent probe

Applied Surface Science, Journal Year: 2025, Volume and Issue: unknown, P. 163380 - 163380

Published: April 1, 2025

Language: Английский

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Theoretical insight into H₂O impact on V₂O₅/TiO₂ catalysts for selective catalytic reduction of NO_x

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(20), P. 14651 - 14663

Published: Jan. 1, 2024

A low level of water in fuel gas enhances the adsorption NH 3 and potentially reaction rate SCR NO x . high decreases Lewis acidity , hinders removal

Language: Английский

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Prediction of Holey Graphyne-Supported Single Atom Catalyst for Nitrogen Reduction Reaction by Interpretable Machine Learning and First-Principles Calculations

Surfaces and Interfaces, Journal Year: 2024, Volume and Issue: 55, P. 105401 - 105401

Published: Nov. 6, 2024

Language: Английский

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