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Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

ACS catalysis, 2019-09, Vol.9 (9), p.7651-7659

2019

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To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Nature communications, 2017-03, Vol.8 (1), p.14621-14621, Article 14621

2017

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Discovery of Acid-Stable Oxygen Evolution Catalysts: High-Throughput Computational Screening of Equimolar Bimetallic Oxides

ACS applied materials & interfaces, 2020-08, Vol.12 (34), p.38256-38265

2020

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Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H2O2 Synthesis through Active Motif Screening

ACS catalysis, 2021-03, Vol.11 (5), p.2483-2491

2021

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Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts

The journal of physical chemistry letters, 2019-08, Vol.10 (15), p.4401-4408

2019

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Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution

Nature catalysis, 2018-09, Vol.1 (9), p.696-703

2018

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Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction

ACS catalysis, 2017-10, Vol.7 (10), p.6600-6608

2017

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Differentiable Optimization for the Prediction of Ground State Structures (DOGSS)

Physical review letters, 2020-10, Vol.125 (17), p.1-173001, Article 173001

2020

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Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks

Journal of chemical information and modeling, 2019-11, Vol.59 (11), p.4742-4749

2019

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Accelerated discovery of CO2 electrocatalysts using active machine learning

Nature (London), 2020-05, Vol.581 (7807), p.178-183

2020

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Methods for comparing uncertainty quantifications for material property predictions

Machine learning: science and technology, 2020-06, Vol.1 (2), p.25006

2020

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Beyond independent error assumptions in large GNN atomistic models

The Journal of chemical physics, 2023-06, Vol.158 (21)

2023

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Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

Catalysis science & technology, 2022-10, Vol.12 (2), p.6256-6267

2022

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Dynamic Workflows for Routine Materials Discovery in Surface Science

Journal of chemical information and modeling, 2018-12, Vol.58 (12), p.2392-2400

2018

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Open Catalyst 2020 (OC20) Dataset and Community Challenges

ACS catalysis, 2021-05, Vol.11 (10), p.6059-6072

2021

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Capturing Structural Transitions in Surfactant Adsorption Isotherms at Solid/Solution Interfaces

Langmuir, 2020-01, Vol.36 (3), p.819-826

2020

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Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning

The journal of physical chemistry letters, 2016-10, Vol.7 (19), p.3931-3935

2016

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Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters

Journal of chemical information and modeling, 2023-10, Vol.63 (20), p.6192-6197

2023

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In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation

Physical chemistry chemical physics : PCCP, 2020-09, Vol.22 (35), p.19454-19458

2020

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Heterogeneous Catalysis in Grammar School

Journal of physical chemistry. C, 2022-02, Vol.126 (6), p.2931-2936

2022

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Computational catalyst discovery: Active classification through myopic multiscale sampling

The Journal of chemical physics, 2021-03, Vol.154 (12), p.124118-124118

2021

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Screening of bimetallic electrocatalysts for water purification with machine learning

The Journal of chemical physics, 2022-08, Vol.157 (7), p.074102-074102

2022

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Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes

Nature communications, 2013, Vol.4 (1), p.2397-2397, Article 2397

2013

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Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes

Nature nanotechnology, 2013-12, Vol.8 (12), p.959-968

2013

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Transfer learning using attentions across atomic systems with graph neural networks (TAAG)

The Journal of chemical physics, 2022-05, Vol.156 (18), p.184702-184702

2022

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Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Discovery of Acid-Stable Oxygen Evolution Catalysts: High-Throughput Computational Screening of Equimolar Bimetallic Oxides

Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H2O2 Synthesis through Active Motif Screening

Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts

Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction

Differentiable Optimization for the Prediction of Ground State Structures (DOGSS)

Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks

Accelerated discovery of CO2 electrocatalysts using active machine learning

Methods for comparing uncertainty quantifications for material property predictions

Beyond independent error assumptions in large GNN atomistic models

Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

Dynamic Workflows for Routine Materials Discovery in Surface Science

Open Catalyst 2020 (OC20) Dataset and Community Challenges

Capturing Structural Transitions in Surfactant Adsorption Isotherms at Solid/Solution Interfaces

Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning

Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters

In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation

Heterogeneous Catalysis in Grammar School

Computational catalyst discovery: Active classification through myopic multiscale sampling

Screening of bimetallic electrocatalysts for water purification with machine learning

Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes

Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes

Transfer learning using attentions across atomic systems with graph neural networks (TAAG)

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