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Fast Prediction of Adsorption Properties for Platinum Nanocatalysts with Generalized Coordination Numbers
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Structure-Sensitive Scaling Relations: Adsorption Energies from Surface Site Stability
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Fluxionality of Subnano Clusters Reshapes the Activity Volcano of Electrocatalysis
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Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning
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Halogen bonding: the σ-hole: Proceedings of “Modeling interactions in biomolecules II”, Prague, September 5th–9th, 2005
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Uncovering electronic and geometric descriptors of chemical activity for metal alloys and oxides using unsupervised machine learning
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Assessing catalytic rates of bimetallic nanoparticles with active-site specificity: A case study using NO decomposition
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VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code
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The Brønsted–Evans–Polanyi relation and the volcano curve in heterogeneous catalysis
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From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis
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An electronic structure descriptor for oxygen reactivity at metal and metal-oxide surfaces
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Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors
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Active Site Fluxional Restructuring as a New Paradigm in Triggering Reaction Activity for Nanocluster Catalysis
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Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks
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Nucleophilic Aromatic Substitution Reactions Described by the Local Electron Attachment Energy
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Computing the Fukui Function in Solid-State Chemistry: Application to Alkaline Earth Oxides Bulk and Surfaces
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Unraveling Electronic Trends in O* and OH* Surface Adsorption in the MO 2 Transition-Metal Oxide Series
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Exploring Structural Diversity and Fluxionality of Pt n ( n = 10–13) Clusters from First-Principles
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Configurational Energies of Nanoparticles Based on Metal–Metal Coordination
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Machine-Learning-Augmented Chemisorption Model for CO 2 Electroreduction Catalyst Screening
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Alchemical Predictions for Computational Catalysis: Potential and Limitations
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Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm
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Oxygen Reduction Reaction: Rapid Prediction of Mass Activity of Nanostructured Platinum Electrocatalysts
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Dynamics of Subnanometer Pt Clusters Can Break the Scaling Relationships in Catalysis
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Predicting Adsorption Properties of Catalytic Descriptors on Bimetallic Nanoalloys with Site-Specific Precision
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Predicting the Oxygen-Binding Properties of Platinum Nanoparticle Ensembles by Combining High-Precision Electron Microscopy and Density Functional Theory
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Open Catalyst 2020 (OC20) Dataset and Community Challenges
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Predicting a Key Catalyst-Performance Descriptor for Supported Metal Nanoparticles: Metal Chemical Potential
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Machine Learning-Assisted Screening of Stepped Alloy Surfaces for C1 Catalysis
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Fluxionality of Catalytic Clusters: When It Matters and How to Address It
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The Role of Roughening to Enhance Selectivity to C 2+ Products during CO 2 Electroreduction on Copper
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The Energetics of Supported Metal Nanoparticles: Relationships to Sintering Rates and Catalytic Activity
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Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces
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