Photocatalytic CO2 reduction

Nature Reviews Methods Primers, Journal Year: 2023, Volume and Issue: 3(1)

Published: Aug. 10, 2023

Language: Английский

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Machine Learning Descriptors for Data‐Driven Catalysis Study

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

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Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis

Nano Today, Journal Year: 2023, Volume and Issue: 49, P. 101802 - 101802

Published: March 10, 2023

Language: Английский

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ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(3), P. 799 - 811

Published: Jan. 18, 2024

The pursuit of designing smart and functional materials is paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, numerous others. Consequently, researchers are actively involved in the development innovative models strategies for design. Recent advancements analytical tools, experimentation, computer technology additionally enhance design possibilities. Notably, data-driven techniques like artificial intelligence machine learning have achieved substantial progress exploring applications within science. One approach, ChatGPT, a large language model, holds transformative potential addressing complex queries. In this article, we explore ChatGPT's understanding science by assigning some simple tasks subareas computational findings indicate that while ChatGPT may make minor errors accomplishing general tasks, it demonstrates capability to learn adapt through human interactions. However, issues output consistency, probable hidden errors, ethical consequences should be addressed.

Language: Английский

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Computational and Machine Learning Methods for CO₂ Capture Using Metal–Organic Frameworks

ACS Nano, Journal Year: 2024, Volume and Issue: 18(35), P. 23842 - 23875

Published: Aug. 22, 2024

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made significant progress provided benefits in the chemistry material science. This work examines interactions between materials computational science at scales for metal-organic framework (MOF) adsorbent development toward carbon dioxide (CO

Language: Английский

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Machine learning for CO2 capture and conversion: A review

Energy and AI, Journal Year: 2024, Volume and Issue: 16, P. 100361 - 100361

Published: March 30, 2024

Coupled electrochemical systems for the direct capture and conversion of CO2 have garnered significant attention owing to their potential enhance energy- cost-efficiency by circumventing amine regeneration step. However, optimizing coupled system is more challenging than handling separated because its complexity, caused incorporation solvent heterogeneous catalysts. Nevertheless, deployment machine learning can be immensely beneficial, reducing both time cost ability simulate describe complex with numerous parameters involved. In this review, we summarized techniques employed in development solvents such as ionic liquids, well To optimize a system, these two separately developed will need combined via future.

Language: Английский

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Coordination environment manipulation of single atom catalysts: Regulation strategies, characterization techniques and applications

Coordination Chemistry Reviews, Journal Year: 2024, Volume and Issue: 515, P. 215952 - 215952

Published: May 20, 2024

Language: Английский

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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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Leveraging machine learning in porous media

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(32), P. 20717 - 20782

Published: Jan. 1, 2024

Evaluating the advantages and limitations of applying machine learning for prediction optimization in porous media, with applications energy, environment, subsurface studies.

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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