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Machine-Learning-Augmented Chemisorption Model for CO 2 Electroreduction Catalyst Screening
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Nature of the Active Sites of Copper Zinc Catalysts for Carbon Dioxide Electroreduction
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Theory-Aided Discovery of Metallic Catalysts for Selective Propane Dehydrogenation to Propylene
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Computational Methods in Heterogeneous Catalysis
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December 2020 |
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Identifying Active Sites of the Water–Gas Shift Reaction over Titania Supported Platinum Catalysts under Uncertainty
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Density functional theory in surface chemistry and catalysis
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January 2011 |
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Managing the Computational Chemistry Big Data Problem: The ioChem-BD Platform
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Machine Learning for Computational Heterogeneous Catalysis
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June 2019 |
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The nature of active sites for carbon dioxide electroreduction over oxide-derived copper catalysts
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January 2021 |
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CO organization at ambient pressure on stepped Pt surfaces: first principles modeling accelerated by neural networks
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January 2021 |
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Composition, structure, and stability of RuO 2 ( 110 ) as a function of oxygen pressure
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December 2001 |
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Beyond Scaling Relations for the Description of Catalytic Materials
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February 2019 |
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Decoding reactive structures in dilute alloy catalysts
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February 2022 |
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Hydrogen Coupling on Platinum Using Artificial Neural Network Potentials and DFT
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October 2021 |
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Data-Driven Descriptor Engineering and Refined Scaling Relations for Predicting Transition Metal Oxide Reactivity
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Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles
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May 2018 |
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Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
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January 2016 |
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Systematic Enumeration of Elementary Reaction Steps in Surface Catalysis
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February 2019 |
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Dynamics of H2O Adsorption on Pt(110)-(1 × 2) Based on a Neural Network Potential Energy Surface
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September 2020 |
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Enabling Catalyst Discovery through Machine Learning and High-Throughput Experimentation
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November 2019 |
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Active Site Representation in First-Principles Microkinetic Models: Data-Enhanced Computational Screening for Improved Methanation Catalysts
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November 2020 |
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Machine learning of lateral adsorbate interactions in surface reaction kinetics
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June 2022 |
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March 2022 |
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Open Catalyst 2020 (OC20) Dataset and Community Challenges
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May 2021 |
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Machine Learning-Assisted Screening of Stepped Alloy Surfaces for C1 Catalysis
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March 2022 |
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Scaling Properties of Adsorption Energies for Hydrogen-Containing Molecules on Transition-Metal Surfaces
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July 2007 |
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Scalable approach to high coverages on oxides via iterative training of a machine‐learning algorithm
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August 2020 |
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Structure and energetics of liquid water–hydroxyl layers on Pt(111)
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January 2022 |
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Metastable Structures in Cluster Catalysis from First-Principles: Structural Ensemble in Reaction Conditions and Metastability Triggered Reactivity
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February 2018 |
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April 2018 |
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Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts
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June 2021 |
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An all-round AI-Chemist with a scientific mind
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September 2022 |
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Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms
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June 2016 |
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An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts
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March 2020 |
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Machine learning–assisted CO 2 utilization in the catalytic dry reforming of hydrocarbons: Reaction pathways and multicriteria optimization analyses
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December 2021 |
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Catalysis-Hub.org, an open electronic structure database for surface reactions
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May 2019 |
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Data‐Driven Interpretable Descriptors for the Structure–Activity Relationship of Surface Lattice Oxygen on Doped Vanadium Oxides
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July 2022 |
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Unraveling the Synergistic Effect of Re and Cs Promoters on Ethylene Epoxidation over Silver Catalysts with Machine Learning-Accelerated First-Principles Simulations
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February 2022 |
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Global optimization of copper clusters at the ZnO(101¯0) surface using a DFT-based neural network potential and genetic algorithms
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August 2020 |
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Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction
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June 2020 |
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Solvent molecules form surface redox mediators in situ and cocatalyze O 2 reduction on Pd
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February 2021 |
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Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials
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April 2014 |
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Data‐Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts
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January 2023 |
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Active learning with non- ab initio input features toward efficient CO 2 reduction catalysts
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January 2018 |
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New design paradigm for heterogeneous catalysts
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April 2015 |
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Using metadynamics to explore complex free-energy landscapes
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March 2020 |
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Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
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August 2022 |
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Strategies to break linear scaling relationships
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October 2019 |
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Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights
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April 2022 |
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To address surface reaction network complexity using scaling relations machine learning and DFT calculations
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March 2017 |
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Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species
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July 2017 |
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Evaluation of a Data-Driven, Machine Learning Approach for Identifying Potential Candidates for Environmental Catalysts: From Database Development to Prediction
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June 2021 |
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Electronic Chemical Potential in Chemisorption and Catalysis
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March 1952 |
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Neural network molecular dynamics simulations of solid–liquid interfaces: water at low-index copper surfaces
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January 2016 |
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The concept of active site in heterogeneous catalysis
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January 2022 |
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A chemistry‐inspired neural network kinetic model for oxidative coupling of methane from high‐throughput data
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January 2022 |
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A machine learning framework for the analysis and prediction of catalytic activity from experimental data
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April 2020 |
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Following the structure of copper-zinc-alumina across the pressure gap in carbon dioxide hydrogenation
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June 2021 |
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Latent Representation Learning for Structural Characterization of Catalysts
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February 2021 |
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Complex‐Solid‐Solution Electrocatalyst Discovery by Computational Prediction and High‐Throughput Experimentation**
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February 2021 |
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Physically informed artificial neural networks for atomistic modeling of materials
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May 2019 |
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Assessing the Influence of Zeolite Composition on Oxygen-Bridged Diamino Dicopper(II) Complexes in Cu-CHA DeNOx Catalysts by Machine Learning-Assisted X-ray Absorption Spectroscopy
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June 2022 |
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Hydrogen bond network connectivity in the electric double layer dominates the kinetic pH effect in hydrogen electrocatalysis on Pt
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September 2022 |
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Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
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October 2017 |
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Chemical Kinetics Bayesian Inference Toolbox (CKBIT)
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August 2021 |
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Simulating Segregation in a Ternary Cu–Pd–Au Alloy with Density Functional Theory, Machine Learning, and Monte Carlo Simulations
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January 2022 |
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Induce to reproduce
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August 2022 |
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Automatic Generation of Microkinetic Mechanisms for Heterogeneous Catalysis
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May 2017 |
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Language-oriented rule-based reaction network generation and analysis: Description of RING
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October 2012 |
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General screening of surface alloys for catalysis
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January 2020 |
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Structure and Dynamics of the Liquid–Water/Zinc-Oxide Interface from Machine Learning Potential Simulations
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December 2018 |
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Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium
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March 2021 |
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Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
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April 2010 |
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Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning
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September 2016 |
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Methanol Synthesis from CO2/CO Mixture on Cu–Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search
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July 2022 |
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Machine learning for heterogeneous catalyst design and discovery
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May 2018 |
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Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys
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November 2020 |
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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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September 2021 |
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Semi-grand canonical Monte Carlo simulation of the acrolein induced surface segregation and aggregation of AgPd with machine learning surrogate models
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April 2021 |
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Subgroup Discovery Points to the Prominent Role of Charge Transfer in Breaking Nitrogen Scaling Relations at Single-Atom Catalysts on VS2
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June 2021 |
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High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane
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December 2019 |
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Elucidation of Cu–Zn Surface Alloying on Cu(997) by Machine-Learning Molecular Dynamics
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June 2022 |
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Towards operando computational modeling in heterogeneous catalysis
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January 2018 |
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Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear‐scaling relationships to statistical learning techniques
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May 2021 |
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CheKiPEUQ Intro 1: Bayesian Parameter Estimation Considering Uncertainty or Error from both Experiments and Theory**
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October 2020 |
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Towards the computational design of solid catalysts
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April 2009 |
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Correlating hydration free energy and specific adsorption of alkali metal cations during CO2 electroreduction on Au
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DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time
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September 2022 |
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Propane Dehydrogenation on Platinum Catalysts: Identifying the Active Sites through Bayesian Analysis
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February 2022 |
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Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
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November 2020 |
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New tolerance factor to predict the stability of perovskite oxides and halides
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February 2019 |
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Dynamic coordination of cations and catalytic selectivity on zinc–chromium oxide alloys during syngas conversion
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June 2019 |
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In Situ Surface Structures of PdAg Catalyst and Their Influence on Acetylene Semihydrogenation Revealed by Machine Learning and Experiment
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April 2021 |
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Methods for comparing uncertainty quantifications for material property predictions
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May 2020 |
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Automated Mechanism Generation Using Linear Scaling Relationships and Sensitivity Analyses Applied to Catalytic Partial Oxidation of Methane
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June 2021 |
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Reaction sampling and reactivity prediction using the stochastic surface walking method
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January 2015 |
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Accelerated discovery of CO2 electrocatalysts using active machine learning
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May 2020 |
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A machine learning approach to graph-theoretical cluster expansions of the energy of adsorbate layers
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August 2017 |
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Free energy of proton transfer at the water–TiO 2 interface from ab initio deep potential molecular dynamics
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SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
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Learning Chemistry of Complex Reaction Systems via a Python First-Principles Reaction Rule Stencil (pReSt) Generator
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July 2021 |
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Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
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December 2022 |
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Rate-Based Construction of Kinetic Models for Complex Systems
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May 1997 |
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Dynamical Study of Adsorbate-Induced Restructuring Kinetics in Bimetallic Catalysts Using the PdAu(111) Model System
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A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells
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Data driven reaction mechanism estimation via transient kinetics and machine learning
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September 2021 |
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Deciphering the Phillips Catalyst by Orbital Analysis and Supervised Machine Learning from Cr Pre-edge XANES of Molecular Libraries
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May 2021 |
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Interpretable Machine Learning of Chemical Bonding at Solid Surfaces
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November 2021 |
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Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics
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August 2020 |
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Covalency competition dominates the water oxidation structure–activity relationship on spinel oxides
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June 2020 |
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Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks
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October 2021 |
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Extracting Knowledge from Data through Catalysis Informatics
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June 2018 |
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Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis
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October 2022 |
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Infusing theory into deep learning for interpretable reactivity prediction
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September 2021 |
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Descriptor-Free Design of Multicomponent Catalysts
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August 2022 |
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Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning
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July 2018 |
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Is the water/Pt(111) interface ordered at room temperature?
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December 2021 |
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A Theory-Guided X-ray Absorption Spectroscopy Approach for Identifying Active Sites in Atomically Dispersed Transition-Metal Catalysts
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November 2021 |
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Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution
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September 2018 |
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Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion
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ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
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Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
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Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
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New Perspectives on CO 2 –Pt(111) Interaction with a High-Dimensional Neural Network Potential Energy Surface
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Insights into Supported Subnanometer Catalysts Exposed to CO via Machine-Learning-Enabled Multiscale Modeling
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Machine learning potentials for extended systems: a perspective
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July 2021 |
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Best practices in machine learning for chemistry
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May 2021 |