Online citations, reference lists, and bibliographies.

First-principles Database Driven Computational Neural Network Approach To The Discovery Of Active Ternary Nanocatalysts For Oxygen Reduction Reaction.

J. Kang, Seung Hyo Noh, Jeemin Hwang, Hoje Chun, H. Kim, Byungchan Han
Published 2018 · Chemistry, Medicine

Cite This
Download PDF
Analyze on Scholarcy
Share
An elegant machine-learning-based algorithm was applied to study the thermo-electrochemical properties of ternary nanocatalysts for oxygen reduction reaction (ORR). High-dimensional neural network potentials (NNPs) for the interactions among the components were parameterized from big dataset established by first-principles density functional theory calculations. The NNPs were then incorporated with Monte Carlo (MC) and molecular dynamics (MD) simulations to identify not only active, but also electrochemically stable nanocatalysts for ORR in acidic solution. The effects of surface strain caused by selective segregation of certain components on the catalytic performance were accurately characterized. The computationally efficient and precise approach proposes a promising ORR candidate: 2.6 nm icosahedron comprising 60% of Pt and 40% Ni/Cu. Our methodology can be applied for high-throughput screening and designing of key functional nanomaterials to drastically enhance the performance of various electrochemical systems.
This paper references
10.1103/PHYSREVLETT.98.146401
Generalized neural-network representation of high-dimensional potential-energy surfaces.
J. Behler (2007)
10.1021/ja903247x
Predicted trends of core-shell preferences for 132 late transition-metal binary-alloy nanoparticles.
Lin-Lin Wang (2009)
10.1021/ja5030172
Tuning nanoparticle structure and surface strain for catalysis optimization.
Sen Zhang (2014)
10.1002/anie.201208487
The catalyst genome.
J. Nørskov (2013)
10.1090/S0025-5718-1970-0274029-X
Conditioning of Quasi-Newton Methods for Function Minimization
D. Shanno (1970)
10.1063/1.3553717
Atom-centered symmetry functions for constructing high-dimensional neural network potentials.
J. Behler (2011)
10.1103/PhysRevB.50.17953
Projector augmented-wave method.
Blöchl (1994)
10.1038/srep27218
Oxygen-Deficient Zirconia (ZrO2−x): A New Material for Solar Light Absorption
A. Sinhamahapatra (2016)
10.1103/PhysRevB.47.558
Ab initio molecular dynamics for liquid metals.
G. Kresse (1993)
10.1021/nl5028205
Multimetallic core/interlayer/shell nanostructures as advanced electrocatalysts.
Yijin Kang (2014)
10.1016/0927-0256(96)00008-0
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
G. Kresse (1996)
10.1021/ACSCATAL.7B01648
Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction
Zachary W. Ulissi (2017)
10.1021/acsnano.7b04097
Radially Phase Segregated PtCu@PtCuNi Dendrite@Frame Nanocatalyst for the Oxygen Reduction Reaction.
Jongsik Park (2017)
10.1021/ja303950v
Icosahedral platinum alloy nanocrystals with enhanced electrocatalytic activities.
Jianbo Wu (2012)
10.1038/nmat3668
Compositional segregation in shaped Pt alloy nanoparticles and their structural behaviour during electrocatalysis.
Chunhua Cui (2013)
10.1021/nl5005674
Understanding the composition and activity of electrocatalytic nanoalloys in aqueous solvents: a combination of DFT and accurate neural network potentials.
Nongnuch Artrith (2014)
10.1007/s12274-015-0839-2
First-principles computational study of highly stable and active ternary PtCuNi nanocatalyst for oxygen reduction reaction
Seung Hyo Noh (2015)
10.1021/cr500519c
Carbon-supported Pt-based alloy electrocatalysts for the oxygen reduction reaction in polymer electrolyte membrane fuel cells: particle size, shape, and composition manipulation and their impact to activity.
Yan-Jie Wang (2015)
10.1103/PhysRevB.54.11169
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.
Kresse (1996)
10.1021/ACS.JPCC.6B12752
Modeling Segregation on AuPd(111) Surfaces with Density Functional Theory and Monte Carlo Simulations
Jacob R. Boes (2017)
10.1021/CS502112G
Octahedral Pt2CuNi Uniform Alloy Nanoparticle Catalyst with High Activity and Promising Stability for Oxygen Reduction Reaction
Changlin Zhang (2015)
10.1021/acsami.5b02572
First-Principles Study on the Thermal Stability of LiNiO2 Materials Coated by Amorphous Al2O3 with Atomic Layer Thickness.
Joonhee Kang (2015)
10.1002/ANIE.200504386
Changing the activity of electrocatalysts for oxygen reduction by tuning the surface electronic structure.
Vojislav Stamenkovic (2006)
10.1093/comjnl/13.3.317
A New Approach to Variable Metric Algorithms
R. Fletcher (1970)
10.1038/AM.2016.142
Towards a comprehensive understanding of FeCo coated with N-doped carbon as a stable bi-functional catalyst in acidic media
Seung Hyo Noh (2016)
10.1016/j.cpc.2016.05.010
Amp: A modular approach to machine learning in atomistic simulations
Alireza Khorshidi (2016)
10.1016/J.JPOWSOUR.2013.03.077
First-principles thermodynamic study of the electrochemical stability of Pt nanoparticles in fuel cell applications
Joon Kyo Seo (2013)
10.1002/cctc.201402248
First Principles Study of Morphology, Doping Level, and Water Solvation Effects on the Catalytic Mechanism of Nitrogen‐Doped Graphene in the Oxygen Reduction Reaction
Do-hyun Kwak (2014)
10.1021/cr040090g
Nanoalloys: from theory to applications of alloy clusters and nanoparticles.
R. Ferrando (2008)
10.1002/cctc.201800310
Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data
K. Takahashi (2018)
10.1090/S0025-5718-1970-0258249-6
A family of variable-metric methods derived by variational means
D. Goldfarb (1970)
10.1016/J.COMPTC.2014.09.017
The origin of enhanced electrocatalytic activity of Pt–M (M = Fe, Co, Ni, Cu, and W) alloys in PEM fuel cell cathodes: A DFT computational study
Lihui Ou (2014)
10.1103/PhysRevLett.77.3865
Generalized Gradient Approximation Made Simple.
Perdew (1996)
10.1093/imamat/6.1.76
The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
C. Broyden (1970)
10.1021/acs.nanolett.7b04007
Roles of Mo Surface Dopants in Enhancing the ORR Performance of Octahedral PtNi Nanoparticles.
Qingying Jia (2018)
10.1126/science.aaa8765
High-performance transition metal–doped Pt3Ni octahedra for oxygen reduction reaction
X. Huang (2015)
10.1021/acs.jpclett.6b01071
First-Principles Design of Graphene-Based Active Catalysts for Oxygen Reduction and Evolution Reactions in the Aprotic Li-O2 Battery.
Joonhee Kang (2016)
10.1002/CCTC.201701929
Front Cover: Mechanism of Carbon Monoxide Dissociation on a Cobalt Fischer–Tropsch Catalyst (ChemCatChem 1/2018)
Wei Chen (2018)
10.1038/ncomms14621
To address surface reaction network complexity using scaling relations machine learning and DFT calculations
Zachary W. Ulissi (2017)
10.1038/nchem.367
Alloys of platinum and early transition metals as oxygen reduction electrocatalysts.
J. Greeley (2009)



This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar