Inter‐Metal Interaction of Dual‐Atom Catalysts in Heterogeneous Catalysis

Angewandte Chemie International Edition, Год журнала: 2023, Номер 62(42)

Опубликована: Июнь 14, 2023

Dual-atom catalysts (DACs) have been a new frontier in heterogeneous catalysis due to their unique intrinsic properties. The synergy between dual atoms provides flexible active sites, promising enhance performance and even catalyze more complex reactions. However, precisely regulating site structure uncovering dual-atom metal interaction remain grand challenges. In this review, we clarify the significance of inter-metal DACs based on understanding center structures. Three diatomic configurations are elaborated, including isolated single-atom, N/O-bridged dual-atom, direct dual-metal bonding interaction. Subsequently, up-to-date progress oxidation reactions, hydrogenation/dehydrogenation electrocatalytic photocatalytic reactions summarized. structure-activity relationship catalytic is then discussed at an atomic level. Finally, challenges future directions engineer discussed. This review will offer prospects for rational design efficient toward catalysis.

Язык: Английский

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Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Advanced Materials, Год журнала: 2024, Номер 36(18)

Опубликована: Янв. 19, 2024

Abstract Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design discovery, reducing need for time‐consuming labor‐intensive experiments simulations. In contrast to their achiral counterparts, application machine chiral nanomaterials is still its infancy, with a limited number publications date. This despite great advance development new sustainable high values optical activity, circularly polarized luminescence, enantioselectivity, as well analysis structural chirality by electron microscopy. this review, an methods used studying provided, subsequently offering guidance on adapting extending work nanomaterials. An overview within framework synthesis–structure–property–application relationships presented insights how leverage study these highly complex are provided. Some key recent reviewed discussed Finally, review captures achievements, ongoing challenges, prospective outlook very important field.

Язык: Английский

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

Chemical Society Reviews, Год журнала: 2024, Номер unknown

Опубликована: Янв. 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.

Язык: Английский

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AI in single-atom catalysts: a review of design and applications

Journal of Materials Informatics, Год журнала: 2025, Номер 5(1)

Опубликована: Фев. 12, 2025

Single-atom catalysts (SACs) have emerged as a research frontier in catalytic materials, distinguished by their unique atom-level dispersion, which significantly enhances activity, selectivity, and stability. SACs demonstrate substantial promise electrocatalysis applications, such fuel cells, CO2 reduction, hydrogen production, due to ability maximize utilization of active sites. However, the development efficient stable involves intricate design screening processes. In this work, artificial intelligence (AI), particularly machine learning (ML) neural networks (NNs), offers powerful tools for accelerating discovery optimization SACs. This review systematically discusses application AI technologies through four key stages: (1) Density functional theory (DFT) ab initio molecular dynamics (AIMD) simulations: DFT AIMD are used investigate mechanisms, with high-throughput applications expanding accessible datasets; (2) Regression models: ML regression models identify features that influence performance, streamlining selection promising materials; (3) NNs: NNs expedite known structural models, facilitating rapid assessment potential; (4) Generative adversarial (GANs): GANs enable prediction novel high-performance tailored specific requirements. work provides comprehensive overview current status insights recommendations future advancements field.

Язык: Английский

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A Surrogate Machine Learning Model for the Design of Single-Atom Catalyst on Carbon and Porphyrin Supports towards Electrochemistry

The Journal of Physical Chemistry C, Год журнала: 2023, Номер 127(21), С. 9992 - 10000

Опубликована: Май 19, 2023

We apply the machine learning (ML) tool to calculate Gibbs free energy (ΔG) of reaction intermediates rapidly and accurately as a guide for designing porphyrin- graphene-supported single-atom catalysts (SACs) toward electrochemical reactions. Based on 2105 DFT calculation data from literature, we trained support vector (SVR) algorithm. The hyperparameters were optimized using Bayesian optimization along with 10-fold cross-validation avoid overfitting. Shapley Additive exPlanation (SHAP) permutation methods, feature importance analysis suggests that most important parameters are number pyridinic nitrogen (Npy), d electrons (θd), valence intermediates. Inspired by this Pearson correlation coefficient, found linear dependent, simple, general descriptor (φ) describe ΔG (e.g., ΔGOH* = 0.020φ – 2.190). Using SVR algorithm, ΔGOH*, ΔGO*, ΔGOOH*, ΔGOO*, ΔGH*, ΔGCOOH*, ΔGCO*, ΔGN2* predicted oxygen reduction (ORR), evolution (OER), hydrogen (HER), CO2 (CO2RR). model predicts an ORR overpotential 0.51 V HER 0.22 FeN4-SAC. Moreover, used algorithm high-throughput screening SACs, suggesting new SACs low overpotentials. This strategy provides data-driven catalyst design method significantly reduces costs calculations while providing means electrocatalysis beyond.

Язык: Английский

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Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective

Advanced Functional Materials, Год журнала: 2023, Номер 34(12)

Опубликована: Дек. 12, 2023

Abstract Heterogenous electrocatalysis continues to witness propagating interest in a plethora of non‐limiting electrochemical fields. Of which, water electrolysis has moved from lab‐scale systems commercial electrolyzers albeit high dependence on historic benchmark noble‐metal based catalysts is still the status quo. Notwithstanding, advances material groups such as single‐atom catalysts, perovskites, high‐entropy alloys, among others continue see an increased toward utilization next‐generation electrolyzers. To that end, progress electrocatalyst discovery techniques revolutionized through synergistically combining density functional theory (DFT) and machine learning (ML) techniques. The success ML herein depends numerous interlinked factors algorithm employed, data availability accuracy, with descriptors being critical encapsulate physicochemical perspectives. Historic frameworks areas other than materials left lack standardization appropriating suitable methods high‐throughput DFT, approaches, feature engineering bridge gap between activity‐structure‐electronic relationships. This review outlines needed considerations DFT calculations, important criteria during filtering out screened surfaces, synergistic approaches utilizing theoretical and/or experimental datasets for formulating effective frameworks. Persisting challenges, perspectives, recommendations thereof are highlighted expedite generalize future work pertaining high‐volume discovery.

Язык: Английский

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Catalysis in the digital age: Unlocking the power of data with machine learning

Wiley Interdisciplinary Reviews Computational Molecular Science, Год журнала: 2024, Номер 14(5)

Опубликована: Сен. 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

Язык: Английский

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Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction

Molecules, Год журнала: 2025, Номер 30(4), С. 759 - 759

Опубликована: Фев. 7, 2025

Hydrogen as an environmentally friendly energy carrier, has many significant advantages, such cleanliness, recyclability, and high calorific value of combustion, which makes it one the major potential sources supply in future. evolution reaction (HER) is important strategy to cope with global shortage environmental degradation, given large cost involved HER, crucial screen develop stable efficient catalysts. Compared traditional catalyst development model, rapid data science technology, especially machine learning shown great field recent years. Among them, research method combining high-throughput computing received extensive attention materials science. Therefore, this paper provides a review on guide HER electrocatalysts, covering application constructing prediction models extracting key features catalytic activity. The future challenges directions are also prospected, aiming provide useful references lessons for related research.

Язык: Английский

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Inter‐Metal Interaction of Dual‐Atom Catalysts in Heterogeneous Catalysis

Angewandte Chemie, Год журнала: 2023, Номер 135(42)

Опубликована: Июнь 14, 2023

Abstract Dual‐atom catalysts (DACs) have been a new frontier in heterogeneous catalysis due to their unique intrinsic properties. The synergy between dual atoms provides flexible active sites, promising enhance performance and even catalyze more complex reactions. However, precisely regulating site structure uncovering dual‐atom metal interaction remain grand challenges. In this review, we clarify the significance of inter‐metal DACs based on understanding center structures. Three diatomic configurations are elaborated, including isolated single‐atom, N/O‐bridged dual‐atom, direct dual‐metal bonding interaction. Subsequently, up‐to‐date progress oxidation reactions, hydrogenation/dehydrogenation electrocatalytic photocatalytic reactions summarized. structure‐activity relationship catalytic is then discussed at an atomic level. Finally, challenges future directions engineer discussed. This review will offer prospects for rational design efficient toward catalysis.

Язык: Английский

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Screening of transition metal dual-atom catalysts for hydrogen evolution reaction based on high-throughput calculation and searching surrogate prediction model using simple features

Applied Surface Science, Год журнала: 2024, Номер 659, С. 159942 - 159942

Опубликована: Март 19, 2024

Язык: Английский

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