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
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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
Generalized neural-network representation of high-dimensional potential-energy surfaces.
J. Behler (2007)
Predicted trends of core-shell preferences for 132 late transition-metal binary-alloy nanoparticles.
Lin-Lin Wang (2009)
Tuning nanoparticle structure and surface strain for catalysis optimization.
Sen Zhang (2014)
The catalyst genome.
J. Nørskov (2013)
Conditioning of Quasi-Newton Methods for Function Minimization
D. Shanno (1970)
Atom-centered symmetry functions for constructing high-dimensional neural network potentials.
J. Behler (2011)
Projector augmented-wave method.
Oxygen-Deficient Zirconia (ZrO2−x): A New Material for Solar Light Absorption
A. Sinhamahapatra (2016)
Ab initio molecular dynamics for liquid metals.
G. Kresse (1993)
Multimetallic core/interlayer/shell nanostructures as advanced electrocatalysts.
Yijin Kang (2014)
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
G. Kresse (1996)
Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction
Zachary W. Ulissi (2017)
Radially Phase Segregated PtCu@PtCuNi Dendrite@Frame Nanocatalyst for the Oxygen Reduction Reaction.
Jongsik Park (2017)
Icosahedral platinum alloy nanocrystals with enhanced electrocatalytic activities.
Jianbo Wu (2012)
Compositional segregation in shaped Pt alloy nanoparticles and their structural behaviour during electrocatalysis.
Chunhua Cui (2013)
Understanding the composition and activity of electrocatalytic nanoalloys in aqueous solvents: a combination of DFT and accurate neural network potentials.
Nongnuch Artrith (2014)
First-principles computational study of highly stable and active ternary PtCuNi nanocatalyst for oxygen reduction reaction
Seung Hyo Noh (2015)
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)
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.
Modeling Segregation on AuPd(111) Surfaces with Density Functional Theory and Monte Carlo Simulations
Jacob R. Boes (2017)
Octahedral Pt2CuNi Uniform Alloy Nanoparticle Catalyst with High Activity and Promising Stability for Oxygen Reduction Reaction
Changlin Zhang (2015)
First-Principles Study on the Thermal Stability of LiNiO2 Materials Coated by Amorphous Al2O3 with Atomic Layer Thickness.
Joonhee Kang (2015)
Changing the activity of electrocatalysts for oxygen reduction by tuning the surface electronic structure.
Vojislav Stamenkovic (2006)
A New Approach to Variable Metric Algorithms
R. Fletcher (1970)
Towards a comprehensive understanding of FeCo coated with N-doped carbon as a stable bi-functional catalyst in acidic media
Seung Hyo Noh (2016)
Amp: A modular approach to machine learning in atomistic simulations
Alireza Khorshidi (2016)
First-principles thermodynamic study of the electrochemical stability of Pt nanoparticles in fuel cell applications
Joon Kyo Seo (2013)
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)
Nanoalloys: from theory to applications of alloy clusters and nanoparticles.
R. Ferrando (2008)
Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data
K. Takahashi (2018)
A family of variable-metric methods derived by variational means
D. Goldfarb (1970)
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)
Generalized Gradient Approximation Made Simple.
The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
C. Broyden (1970)
Roles of Mo Surface Dopants in Enhancing the ORR Performance of Octahedral PtNi Nanoparticles.
Qingying Jia (2018)
High-performance transition metal–doped Pt3Ni octahedra for oxygen reduction reaction
X. Huang (2015)
First-Principles Design of Graphene-Based Active Catalysts for Oxygen Reduction and Evolution Reactions in the Aprotic Li-O2 Battery.
Joonhee Kang (2016)
Front Cover: Mechanism of Carbon Monoxide Dissociation on a Cobalt Fischer–Tropsch Catalyst (ChemCatChem 1/2018)
Wei Chen (2018)
To address surface reaction network complexity using scaling relations machine learning and DFT calculations
Zachary W. Ulissi (2017)
Alloys of platinum and early transition metals as oxygen reduction electrocatalysts.
J. Greeley (2009)
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Structural search for stable Mg-Ca alloys accelerated with a neural network interatomic model.
Wilfredo Ibarra-Hernández (2018)
On machine learning force fields for metallic nanoparticles
Claudio Zeni (2019)
Size‐Extensive Molecular Machine Learning with Global Representations
Hyunwook Jung (2020)
First-principles computational approach for innovative design of highly functional electrocatalysts in fuel cells
Seunghyo Noh (2018)
Multitribe evolutionary search for stable Cu-Pd-Ag nanoparticles using neural network models.
Samad Hajinazar (2019)
Progress in Computational and Machine-Learning Methods for Heterogeneous Small-Molecule Activation.
G. H. Gu (2020)