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Theoretical and Computational Chemistry

Working Paper

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

Seoin Back, Kevin Tran, Zachary Ulissi

Version 1 posted 02 April 2019

1,611

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Catalysis

Working Paper

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

Seoin Back, Jonggeol Na, Zachary Ulissi

Version 1 posted 13 October 2020

1,354

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Theoretical and Computational Chemistry

Working Paper

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

Seoin Back, Junwoong Yoon, Nianhan Tian, Wen Zhong, Kevin Tran, Zachary Ulissi

Version 1 posted 21 May 2019

1,116

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Materials Chemistry

Working Paper

Chemically Inspired Convolutional Neural Network using Electronic Structure Representation

Dong Hyeon Mok, Daeun Shin, Jonggeol Na, Seoin Back

Version 1 posted 30 January 2023

460

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Catalysis

Working Paper

Direction-based Graph Representation to Accelerate Stable Catalysts Discovery

Dong Hyeon Mok, Jongseung Kim, Seoin Back

Version 1 posted 30 September 2022

400

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Theoretical and Computational Chemistry

Working Paper

Atomic Structure-free Representation of Active Motifs for Expedited Catalyst Discovery

Dong Hyeon Mok, Seoin Back

Version 2 posted 12 May 2021

448

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Catalysis

Working Paper

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

Seoin Back, Kevin Tran, Zachary Ulissi

Version 1 posted 10 April 2020

1,399

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Catalysis

Working Paper

Understanding Catalytic Activity Trends of Electrochemical Ammonia Oxidation Reaction using Density Functional Theory Calculations and Microkinetic Modeling

Ho Yeon Jang, Seoin Back

Version 1 posted 23 December 2021

722

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8 results in Seoin Back in Authors: Seoin Back

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

An 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

Chemically Inspired Convolutional Neural Network using Electronic Structure Representation

Direction-based Graph Representation to Accelerate Stable Catalysts Discovery

Atomic Structure-free Representation of Active Motifs for Expedited Catalyst Discovery

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

Understanding Catalytic Activity Trends of Electrochemical Ammonia Oxidation Reaction using Density Functional Theory Calculations and Microkinetic Modeling

8 results in Authors: Seoin Back