| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Nørskov, Jens Kehlet
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (32/32 displayed)
- 2023Continuous-flow electrosynthesis of ammonia by nitrogen reduction and hydrogen oxidationcitations
- 2022Reversible Atomization and Nano-Clustering of Pt as a Strategy for Designing Ultra-Low-Metal-Loading Catalystscitations
- 2020Subsurface Nitrogen Dissociation Kinetics in Lithium Metal from Metadynamicscitations
- 2020Nitride or Oxynitride? Elucidating the Composition–Activity Relationships in Molybdenum Nitride Electrocatalysts for the Oxygen Reduction Reactioncitations
- 2019Electro-Oxidation of Methane on Platinum under Ambient Conditionscitations
- 2019An electronic structure descriptor for oxygen reactivity at metal and metal-oxide surfacescitations
- 2019Efficient Pourbaix diagrams of many-element compoundscitations
- 2017Machine-learning methods enable exhaustive searches for active Bimetallic facets and reveal active site motifs for CO2 reductioncitations
- 2017Rh-MnO Interface Sites Formed by Atomic Layer Deposition Promote Syngas Conversion to Higher Oxygenatescitations
- 2017Mechanistic insights into heterogeneous methane activationcitations
- 2016Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learningcitations
- 2015Surface Tension Effects on the Reactivity of Metal Nanoparticlescitations
- 2014Hydrogen adsorption on bimetallic PdAu(111) surface alloyscitations
- 2014Discovery of a Ni-Ga catalyst for carbon dioxide reduction to methanolcitations
- 2014Nanoscale limitations in metal oxide electrocatalysts for oxygen evolutioncitations
- 2013First Principles Investigation of Zinc-anode Dissolution in Zinc-air Batteriescitations
- 2013Theoretical investigation of the activity of cobalt oxides for the electrochemical oxidation of watercitations
- 2013Direct observation of the oxygenated species during oxygen reduction on a platinum fuel cell cathodecitations
- 2013Density functional theory studies of transition metal nanoparticles in catalysis
- 2012CO hydrogenation to methanol on Cu–Ni catalystscitations
- 2012Universality in Oxygen Reduction Electrocatalysis on Metal Surfacescitations
- 2011Electrical conductivity in Li2O2 and its role in determining capacity limitations in non-aqueous Li-O2 batteriescitations
- 2011Trends in Metal Oxide Stability for Nanorods, Nanotubes, and Surfacescitations
- 2010Ammonia dynamics in magnesium ammine from DFT and neutron scatteringcitations
- 2010Ammonia dynamics in magnesium ammine from DFT and neutron scatteringcitations
- 2009Combinatorial Density Functional Theory-Based Screening of Surface Alloys for the Oxygen Reduction Reactioncitations
- 2009A CATALYST, A PROCESS FOR SELECTIVE HYDROGENATION OF ACETYLENE TO ETHYLENE AND A METHOD FOR THE MANUFACTURE OF THE CATALYST
- 2008Identification of non-precious metal alloy catalysts for selective hydrogenation of acetylenecitations
- 2007Discovery of technical methanation catalysts based on computational screeningcitations
- 2007Discovery of technical methanation catalysts based on computational screeningcitations
- 2007CO methanation over supported bimetallic Ni-Fe catalysts: From computational studies towards catalyst optimizationcitations
- 2003The stability of the hydroxylated (0001) surface of alpha-Al2O3citations
Places of action
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article
Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning
Abstract
<p>Surface phase diagrams are necessary for understanding surface chemistry in electrochemical catalysis, where a range of adsorbates and coverages exist at varying applied potentials. These diagrams are typically constructed using intuition, which risks missing complex coverages and configurations at potentials of interest. More accurate cluster expansion methods are often difficult to implement quickly for new surfaces. We adopt a machine learning approach to rectify both issues. Using a Gaussian process regression model, the free energy of all possible adsorbate coverages for surfaces is predicted for a finite number of adsorption sites. Our result demonstrates a rational, simple, and systematic approach for generating accurate free-energy diagrams with reduced computational resources. The Pourbaix diagram for the IrO<sub>2</sub>(110) surface (with nine coverages from fully hydrogenated to fully oxygenated surfaces) is reconstructed using just 20 electronic structure relaxations, compared to approximately 90 using typical search methods. Similar efficiency is demonstrated for the MoS<sub>2</sub> surface.</p>