4.7 Article

Bilayer MN4-O-MN4 by bridge-bonded oxygen ligands: Machine learning to accelerate the design of bifunctional electrocatalysts

Related references

Note: Only part of the references are listed.
Article Nanoscience & Nanotechnology

Transition Metal and N Doping on AIP Monolayers for Bifunctional Oxygen Electrocatalysts: Density Functional Theory Study Assisted by Machine Learning Description

Xuefei Liu et al.

Summary: Efficient bifunctional oxygen evolution/reduction reaction electrocatalysts are crucial for sustainable and renewable clean energy. The study found that substituting P atoms in the AlP system enhances catalytic activity, with machine learning algorithms revealing the origins of activity.

ACS APPLIED MATERIALS & INTERFACES (2022)

Article Chemistry, Multidisciplinary

Sublayer-enhanced atomic sites of single atom catalysts through in situ atomization of metal oxide nanoparticles

Xing Wu et al.

Summary: Carbon supported single atom catalysts (SACs) have shown great potential as effective electrocatalysts in energy storage and conversion devices. This research demonstrates that by controlling the layers of single atom active sites, the catalysts exhibit enhanced activity in the oxygen reduction reaction (ORR), leading to higher turnover frequency (TOF) and reducing the reaction overpotential. The newly-developed catalysts with sublayer-enhanced active sites also show significantly improved performance in alkaline and acidic conditions, making them promising for applications in Zn-air batteries and fuel cells.

ENERGY & ENVIRONMENTAL SCIENCE (2022)

Article Chemistry, Physical

Coordination environments tune the activity of oxygen catalysis on single atom catalysts: A computational study

Gaofan Xiao et al.

Summary: By utilizing density functional theory calculations, this research systematically studied the ORR/OER performances of nitrogen-coordinated transition metal carbon materials and found that asymmetric tri-coordinated catalysts, particularly NiN3-C, exhibit stronger catalytic intermediate adsorption capacity and lower overpotential, making them highly efficient bifunctional catalysts for oxygen catalysis. The enhanced effect is attributed to additional orbital interaction, as evidenced by changes in d-band center and integral crystal orbital Hamilton population. This study not only provides a potential bifunctional oxygen catalyst, but also enriches knowledge in coordination engineering for tailoring the activity of single-atom catalysts.

NANO RESEARCH (2022)

Article Chemistry, Multidisciplinary

Data-Driven High-Throughput Rational Design of Double-Atom Catalysts for Oxygen Evolution and Reduction

Lianping Wu et al.

Summary: A data-driven high-throughput design principle is proposed to evaluate the stability and activity of double-atom catalysts (DACs) for oxygen evolution and oxygen reduction reactions. This approach not only yields remarkable prediction precision but also significantly reduces the screening time compared to pure density functional theory calculations.

ADVANCED FUNCTIONAL MATERIALS (2022)

Article Chemistry, Physical

High-throughput screening of carbon-supported single metal atom catalysts for oxygen reduction reaction

Yiran Wang et al.

Summary: By utilizing density functional theory (DFT), this study investigated the oxygen reduction reaction (ORR) catalytic properties of 180 types of single-atom catalysts (SACs) and found that the adsorption free energy of OH* serves as a universal descriptor for predicting ORR catalytic activity accurately. The research revealed that phthalocyanine, N-coordination graphene, and metal-organic frameworks exhibit relatively lower overpotentials, making them promising supports for single metal atom. Additionally, Co-doped metal-organic frameworks, Ir-doped phthalocyanine, Co-doped N-coordination graphene, Co-doped graphdiyne, and Rh-doped phthalocyanine show extremely low overpotentials comparable to Pt (111). This study provides valuable guidance for the design and selection of carbon-supported SACs for the oxygen reduction reaction.

NANO RESEARCH (2022)

Article Chemistry, Physical

Establishing a theoretical insight for penta-coordinated iron-nitrogen-carbon catalysts toward oxygen reaction

Ruihu Lu et al.

Summary: Penta-coordinated Fe-N-C catalysts have lower theoretical overpotential, and using X ligands for axial coordination can decrease the orbital hybridization of Fe active center with ORR-relevant intermediates, thus accelerating the ORR. The catalytic activity of FeNC-Xs increases with a decreased electronegativity of X ligands, providing a description for the axial coordination effect.

NANO RESEARCH (2022)

Article Chemistry, Multidisciplinary

Engineering the Local Coordination Environment and Density of FeN4 Sites by Mn Cooperation for Electrocatalytic Oxygen Reduction

Huizhu Cai et al.

Summary: This study investigates the strategy of enhancing the electrocatalytic oxygen reduction reaction (ORR) performance by modulating the local environment and density of FeN4 active sites. The results show that by integrating a second metal Mn with Fe to construct Fe&Mn/N-C catalysts, the density of FeN4 active sites can be enhanced and the electronic structure can be modulated, leading to a decrease in the energy barrier of ORR and improved ORR performance.

SMALL (2022)

Article Multidisciplinary Sciences

Iron atom-cluster interactions increase activity and improve durability in Fe-N-C fuel cells

Xin Wan et al.

Summary: This study demonstrates the high activity and stability of an Fe-N-C catalyst for oxygen reduction reaction in acidic fuel cells by introducing nitrogen-coordinated iron clusters and closely surrounding Fe-N-4 active sites. The strong electronic interaction between the iron clusters and the Fe-N-4 sites optimizes the adsorption strength of reaction intermediates and enhances the catalyst's turnover frequency and demetalation resistance.

NATURE COMMUNICATIONS (2022)

Article Engineering, Environmental

Revealing the oxygen Reduction/Evolution reaction activity origin of Carbon-Nitride-Related Single-Atom catalysts: Quantum chemistry in artificial intelligence

Xuhao Wan et al.

Summary: By utilizing density functional theory and machine learning methods, a model has been established to predict the superior electrocatalytic performance of single-atom catalysts. Through experimental validation, RhPc, Co-N-C, and Rh-C4N3 were identified as oxygen electrocatalysts with higher activity. Further analysis identified the electron number of d orbital of the metal active site as the most effective descriptor.

CHEMICAL ENGINEERING JOURNAL (2022)

Article Chemistry, Physical

Unraveling the mechanism of ligands regulating electronic structure of MN4 sites with optimized ORR catalytic performance

Bing Li et al.

Summary: As an alternative to Pt/C catalysts for the oxygen reduction reaction (ORR), metal-nitrogen-carbon (M-N-C) catalysts have emerged as promising materials. However, the ORR catalytic mechanism of M-N-C catalysts is still unclear. In this work, oxygen-containing ligands are introduced to better explain the activity sequence of Fe/Co/MnN4 and Pt/C. It is found that a self-modification mechanism in M-N-C materials can adjust the adsorption for intermediates, facilitating the ORR process. Furthermore, the relationship between electronic structure and catalytic activity is explored using the integrated crystal orbital Hamilton population (iCOHP) as the ORR activity descriptor.

APPLIED SURFACE SCIENCE (2022)

Review Chemistry, Physical

Recent advances in knowledge discovery for heterogeneous catalysis using machine learning

M. Erdem Gunay et al.

Summary: In recent years, the use of machine learning in catalysis has significantly increased due to advances in data processing technologies and the accumulation of a wealth of data in published literature and databases. Researchers analyze data using various machine learning techniques to discover knowledge, develop prediction models, and derive rules for the future. This communication aims to review works involving knowledge discovery in catalysis using machine learning techniques, while also summarizing the basic principles, common tools, and implementation of machine learning in catalysis.

CATALYSIS REVIEWS-SCIENCE AND ENGINEERING (2021)

Article Chemistry, Physical

Two-dimensional IrN2 monolayer: An efficient bifunctional electrocatalyst for oxygen reduction and oxygen evolution reactions

Jingjing Jia et al.

Summary: A novel type of two-dimensional monolayer with hypercoordinate structure was proposed as electrocatalysts for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Among these, the IrN2 monolayer was identified as an ideal bifunctional electrocatalyst for ORR/OER, showing comparable performance to traditional catalysts like Pt and Ir- or Ru-based oxides. This research enriches the field of 2D nanomaterials with hypercoordinate structure and opens up new possibilities for developing efficient oxygen electrocatalysts.

JOURNAL OF COLLOID AND INTERFACE SCIENCE (2021)

Article Multidisciplinary Sciences

Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts

Lianping Wu et al.

Summary: This study demonstrates the use of machine learning for predicting the overpotential of the oxygen evolution reaction, resulting in a model that maps atomic properties with overpotentials and provides high prediction accuracy and significantly reduced prediction time.

ISCIENCE (2021)

Article Chemistry, Physical

Machine-Learning-Accelerated Catalytic Activity Predictions of Transition Metal Phthalocyanine Dual-Metal-Site Catalysts for CO2 Reduction

Xuhao Wan et al.

Summary: The study explores a machine learning-accelerated method based on DFT-ML to efficiently predict the catalytic activity of transition metal phthalocyanine DMSCs for CO2RR. The results demonstrate the potential of using Ag-MoPc as a promising CO2RR electrocatalyst, shedding light on accelerating the rational design of efficient catalysts for energy conversion and conservation.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

Article Nanoscience & Nanotechnology

Transition Metal and N Doping on AlP Monolayers for Bifunctional Oxygen Electrocatalysts: Density Functional Theory Study Assisted by Machine Learning Description

Xuefei Liu et al.

Summary: Researchers proposed a single transition-metal-based defective AlP system for validating bifunctional oxygen electrocatalysis, which exhibited excellent catalytic performance. They further presented the results by establishing volcano plots and contour maps, suggesting that the d-band center and the product of the number of d-orbital electrons and electronegativity of the TM atom are ideal descriptors for this system.

ACS APPLIED MATERIALS & INTERFACES (2021)

Article Chemistry, Physical

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Jiayan Xu et al.

Summary: Heterogeneous catalysis is crucial in the chemical industry, with understanding reactions over surfaces being essential for designing new catalysts. Machine learning can utilize reaction data to mimic the output of ab initio methods for quicker reaction prediction, offering a promising alternative to full ab initio calculations.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2021)

Article Multidisciplinary Sciences

Conversion of non-van der Waals solids to 2D transition-metal chalcogenides

Zhiguo Du et al.

NATURE (2020)

Article Chemistry, Physical

A study on the hydrogen storage performance of graphene-Pd(T)-graphene structure

Weizhi Tian et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2020)

Article Chemistry, Physical

High-Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXene Materials

Jingnan Zheng et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2020)

Article Chemistry, Physical

Study on the hydrogen storage performance of graphene(N)-Sc-graphene(N) structure

Hong Cui et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2020)

Article Chemistry, Multidisciplinary

Self-Adjusting Activity Induced by Intrinsic Reaction Intermediate in Fe-N-C Single-Atom Catalysts

Yu Wang et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2019)

Article Chemistry, Multidisciplinary

Improved Oxygen Reduction Activity in Heteronuclear FeCo-Codoped Graphene: A Theoretical Study

Yanan Meng et al.

ACS SUSTAINABLE CHEMISTRY & ENGINEERING (2019)

Article Chemistry, Physical

Implicit self-consistent electrolyte model in plane-wave density-functional theory

Kiran Mathew et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

Article Chemistry, Physical

A universal principle for a rational design of single-atom electrocatalysts

Haoxiang Xu et al.

NATURE CATALYSIS (2018)

Article Chemistry, Multidisciplinary

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

Austin D. Sendek et al.

ENERGY & ENVIRONMENTAL SCIENCE (2017)

Article Chemistry, Physical

The path towards sustainable energy

Steven Chu et al.

NATURE MATERIALS (2017)

Review Chemistry, Multidisciplinary

Single-Atom Electrocatalysts

Chengzhou Zhu et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2017)

Review Chemistry, Multidisciplinary

Electrocatalysis for the oxygen evolution reaction: recent development and future perspectives

Nian-Tzu Suen et al.

CHEMICAL SOCIETY REVIEWS (2017)

Article Chemistry, Multidisciplinary

Porous cobalt-iron nitride nanowires as excellent bifunctional electrocatalysts for overall water splitting

Yanyong Wang et al.

CHEMICAL COMMUNICATIONS (2016)

Article Chemistry, Multidisciplinary

Machine-learning prediction of the d-band center for metals and bimetals

Ichigaku Takigawa et al.

RSC ADVANCES (2016)

Article Chemistry, Multidisciplinary

Nanoporous Graphene with Single-Atom Nickel Dopants: An Efficient and Stable Catalyst for Electrochemical Hydrogen Production

H. -J. Qiu et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2015)

Review Chemistry, Multidisciplinary

Toward the rational design of non-precious transition metal oxides for oxygen electrocatalysis

Wesley T. Hong et al.

ENERGY & ENVIRONMENTAL SCIENCE (2015)

Article Chemistry, Physical

From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis

Andrew J. Medford et al.

JOURNAL OF CATALYSIS (2015)

Article Multidisciplinary Sciences

Atomic cobalt on nitrogen-doped graphene for hydrogen generation

Huilong Fei et al.

NATURE COMMUNICATIONS (2015)

Article Chemistry, Physical

Implicit solvation model for density-functional study of nanocrystal surfaces and reaction pathways

Kiran Mathew et al.

JOURNAL OF CHEMICAL PHYSICS (2014)

Article Chemistry, Multidisciplinary

High-performance Ag-Co alloy catalysts for electrochemical oxygen reduction

Adam Holewinski et al.

NATURE CHEMISTRY (2014)

Review Chemistry, Multidisciplinary

Progress, Challenges, and Opportunities in Two-Dimensional Materials Beyond Graphene

Sheneve Z. Butler et al.

ACS NANO (2013)

Article Chemistry, Physical

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

Katja Hansen et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)

Article Multidisciplinary Sciences

Catalytically active single-atom niobium in graphitic layers

Xuefeng Zhang et al.

NATURE COMMUNICATIONS (2013)

Article Physics, Multidisciplinary

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Matthias Rupp et al.

PHYSICAL REVIEW LETTERS (2012)

Article Chemistry, Physical

Finding Nature's Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

Geoffroy Hautier et al.

CHEMISTRY OF MATERIALS (2010)

Article Chemistry, Physical

Origin of the overpotential for oxygen reduction at a fuel-cell cathode

JK Norskov et al.

JOURNAL OF PHYSICAL CHEMISTRY B (2004)

Article Computer Science, Interdisciplinary Applications

Stochastic gradient boosting

JH Friedman

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2002)

Article Statistics & Probability

Greedy function approximation: A gradient boosting machine

JH Friedman

ANNALS OF STATISTICS (2001)