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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

1,071

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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

1,236

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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

1,552

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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

401

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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

643

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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

1,299

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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

347

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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

415

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8 results in Seoin Back in Authors: Seoin Back

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

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

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

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

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

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

Direction-based Graph Representation to Accelerate Stable Catalysts Discovery

Chemically Inspired Convolutional Neural Network using Electronic Structure Representation

8 results in Authors: Seoin Back