Literature DB >> 29553728

Chemical Pressure-Driven Enhancement of the Hydrogen Evolving Activity of Ni2P from Nonmetal Surface Doping Interpreted via Machine Learning.

Robert B Wexler1, John Mark P Martirez2, Andrew M Rappe1.   

Abstract

The activity of Ni2P catalysts for the hydrogen evolution reaction (HER) is currently limited by strong H adsorption at the Ni3-hollow site. We investigate the effect of surface nonmetal doping on the HER activity of the Ni3P2 termination of Ni2P(0001), which is stable at modest electrochemical conditions. Using density functional theory (DFT) calculations, we find that both 2 p nonmetals and heavier chalcogens provide nearly thermoneutral H adsorption at moderate surface doping concentrations. We also find, however, that only chalcogen substitution for surface P is exergonic. For intermediate surface concentrations of S, the free energy of H adsorption at the Ni3-hollow site is -0.11 eV, which is significantly more thermoneutral than the undoped surface (-0.45 eV). We use the regularized random forest machine learning algorithm to discover the relative importance of structure and charge descriptors, extracted from the DFT calculations, in determining the HER activity of Ni2P(0001) under different doping concentrations. We discover that the Ni-Ni bond length is the most important descriptor of HER activity, which suggests that the nonmetal dopants induce a chemical pressure-like effect on the Ni3-hollow site, changing its reactivity through compression and expansion.

Entities:  

Year:  2018        PMID: 29553728     DOI: 10.1021/jacs.8b00947

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   15.419


  8 in total

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3.  Graph Convolutional Network-Based Screening Strategy for Rapid Identification of SARS-CoV-2 Cell-Entry Inhibitors.

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5.  Predicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine Learning.

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Review 6.  Design principles of noble metal-free electrocatalysts for hydrogen production in alkaline media: combining theory and experiment.

Authors:  Hyeonjung Jung; Seokhyun Choung; Jeong Woo Han
Journal:  Nanoscale Adv       Date:  2021-10-19

7.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Authors:  John A Keith; Valentin Vassilev-Galindo; Bingqing Cheng; Stefan Chmiela; Michael Gastegger; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Chem Rev       Date:  2021-07-07       Impact factor: 60.622

8.  Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.

Authors:  Marc O J Jäger; Yashasvi S Ranawat; Filippo Federici Canova; Eiaki V Morooka; Adam S Foster
Journal:  ACS Comb Sci       Date:  2020-11-04       Impact factor: 3.784

  8 in total

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