Computational design of (100) alloy surfaces for the hydrogen evolution reaction

Li, Hao; Xu, Shaopeng; Wang, Min; Chen, Ziheng; Ji, Fengfeng; Cheng, Kewei; Gao, Zhengyang; Ding, Zhao; Yang, Weijie

doi:10.1039/d0ta04615a

Cited by 53 publications

(34 citation statements)

References 59 publications

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“…It is, therefore, quite convenient to construct a large dataset containing the energetic information of a class of functional materials by DFT calculations, where ML models can then be adopted to construct a scaling relationship between the structural and energetic properties of the materials. This scheme is widely applied in designing electrochemical catalysts [43] for the hydrogen evolution reaction, [44] oxygen evolution/reduction reaction, [45] nitrogen reduction reaction, [46] and carbon dioxide reduction reaction [47] . As a result, ML is effective in promoting the development of metal–air batteries.…”

Section: Microscale and Mesoscale Simulationsmentioning

confidence: 99%

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Chen

Shen

et al. 2021

Angewandte Chemie

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Emerging machine learning (ML) methods are widely applied in chemistry and materials science studies and have led to a focus on data‐driven research. This Minireview summarizes the application of ML to rechargeable batteries, from the microscale to the macroscale. Specifically, ML offers a strategy to explore new functionals for density functional theory calculations and new potentials for molecular dynamics simulations, which are expected to significantly enhance the challenging descriptions of interfaces and amorphous structures. ML also possesses a great potential to mine and unveil valuable information from both experimental and theoretical datasets. A quantitative “structure–function” correlation can thus be established, which can be used to predict the ionic conductivity of solids as well as the battery lifespan. ML also exhibits great advantages in strategy optimization, such as fast‐charge procedures. The future combination of multiscale simulations, experiments, and ML is also discussed and the role of humans in data‐driven research is highlighted.

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Section: Microscale and Mesoscale Simulationsmentioning

confidence: 99%

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Chen

Shen

et al. 2021

Angewandte Chemie

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“…This kind of databases will be the standard reference for the future research. In computational chemistry, the various DFT methods have been implemented and studied in several systems including chemosensor [22,23], hydrogen evolution reaction (HER) [24][25][26], oxygen reduction reaction (ORR) [27,28], oxygen evolution reaction (OER) [29], molecular machines [30,31], DNA mutation [32][33][34], selective etching [35,36], atomic layer deposition (ALD) [37,38] etc. Although a number of applications of DFT were reported, this book chapter precisely focused on heavy metal sensor and hydrogen evolution reaction.…”

Section: Introductionmentioning

confidence: 99%

Applications of Density Functional Theory on Heavy Metal Sensor and Hydrogen Evolution Reaction (HER)

Srinivasadesikan¹,

Varadaraju²,

Raghunath³

et al. 2022

Density Functional Theory - Recent Advances, New Perspectives and Applications

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A great effort has been devoted to develop the numerical methods to solve Schrödinger equation for atoms and molecules which help to reveal the physico-chemical process and properties of various known/unknown materials. Designing the efficient probe to sense the heavy metals is a crucial process in chemistry. And, during this energy crisis, to find the effective conversion materials for water splitting is an important approach. The density functional theory (DFT) is a powerful tool to identify such materials and made great achievements in the field of heavy metal chemosensor and photocatalysis. Particularly, DFT helps to design the chemosensor for the effective sensor applications. The universe is moving towards the exhaustion of fossil fuels in a decade and so on, DFT plays a vital role to find the green energetic alternative to fossil fuel which is the Hydrogen energy. This book chapter will focus on the application of DFT deliberately on the heavy metal sensors and hydrogen evolution reaction.

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“…For instance, Li et al. designed Pd x Ag 1‐ x and Pd x Au 1‐ x alloy with promising theoretical HER activity in acidic media [28] . Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Insights on Au‐based Bimetallic Alloy Electrocatalysts for Nitrogen Reduction Reaction with High Selectivity and Activity

Shi

Xiang

et al. 2021

ChemSusChem

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Electrochemical reduction of nitrogen to produce ammonia at moderate conditions in aqueous solutions holds great prospect but also faces huge challenges. Considering the high selectivity of Au‐based materials to inhibit competitive hydrogen evolution reaction (HER) and high activity of transition metals such as Fe and Mo toward the nitrogen reduction reaction (NRR), it was proposed that Au‐based alloy materials could act as efficient catalysts for N2 fixation based on density functional theory simulations. Only on Mo3Au(111) surface the adsorption of N2 is stronger than H atom. Thermodynamics combined with kinetics studies were performed to investigate the influence of composition and ratio of Au‐based alloys on NRR and HER. The binding energy and reorganization energy affected performance for the initial N2 activation and hydrogenation process. By considering the free‐energy diagram, the computed potential‐determining step was either the first or the fifth hydrogenation step on metal catalysts. The optimum catalytic activity could be achieved by adjusting atomic proportion in alloys to make all intermediate species exhibit moderate adsorption. Free‐energy diagrams of N2 hydrogenation via Langmuir‐Hinshelwood mechanism and hydrogen evolution via Tafel mechanism were compared to reveal that the Mo3Au surface showed satisfactory catalytic performance by simultaneously promoting NRR and suppressing HER. Theoretical simulations demonstrated that Au‐Mo alloy materials could be applied as high‐performance electrocatalysts for NRR.

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Computational design of (100) alloy surfaces for the hydrogen evolution reaction

Abstract: With the rapid development of kinetically-controlled techniques, synthesis of cubic bimetalllic catalysts with tunable component and composition becomes possible. In recent years, many of the bimetallic alloy catalysts with (100)...

Cited by 53 publications

References 59 publications

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Applications of Density Functional Theory on Heavy Metal Sensor and Hydrogen Evolution Reaction (HER)

Theoretical Insights on Au‐based Bimetallic Alloy Electrocatalysts for Nitrogen Reduction Reaction with High Selectivity and Activity

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