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Theoretical and Computational Chemistry

Working Paper

Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Chenru Duan, Fang Liu, Aditya Nandy, Heather Kulik

604

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Catalysis

Working Paper

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

Aditya Nandy, Chenru Duan, Conrad Goffinet, Heather Kulik

291

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1

Citations

Theoretical and Computational Chemistry

Working Paper

Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening

Fang Liu, Chenru Duan, Heather Kulik

718

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Theoretical and Computational Chemistry

Working Paper

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

Chenru Duan, Fang Liu, Aditya Nandy, Heather Kulik

729

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Catalysis

Working Paper

Understanding the Role of Geometric and Electronic Structure in Bioinspired Catalyst Design: the Case of Formate Dehydrogenase

Mingjie Liu, Azadeh Nazemi, Michael Taylor, Aditya Nandy, Chenru Duan, Adam Steeves, Heather Kulik

195

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Theoretical and Computational Chemistry

Working Paper

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation

Aditya Nandy, Jiazhou Zhu, Jon Paul Janet, Chenru Duan, Rachel Getman, Heather Kulik

761

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1

Citations

Theoretical and Computational Chemistry

Working Paper

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Aditya Nandy, Chenru Duan, Jon Paul Janet, Stefan Gugler, Heather Kulik

955

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1

Citations

Theoretical and Computational Chemistry

Working Paper

Accurate Multi-Objective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

Jon Paul Janet, Sahasrajit Ramesh, Chenru Duan, Heather Kulik

785

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Theoretical and Computational Chemistry

Working Paper

A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery

Jon Paul Janet, Chenru Duan, Tzuhsiung Yang, Aditya Nandy, Heather Kulik

1,660

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1

Citations

Theoretical and Computational Chemistry

Working Paper

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

Chenru Duan, Jon Paul Janet, Fang Liu, Aditya Nandy, Heather Kulik

1,110

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11 results in Chenru Duan in Authors: Chenru Duan

Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

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New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

View abstract keyboard_arrow_down

Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening

View abstract keyboard_arrow_down

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

View abstract keyboard_arrow_down

Understanding the Role of Geometric and Electronic Structure in Bioinspired Catalyst Design: the Case of Formate Dehydrogenase

View abstract keyboard_arrow_down

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation

View abstract keyboard_arrow_down

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

View abstract keyboard_arrow_down

Accurate Multi-Objective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

View abstract keyboard_arrow_down

A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery

View abstract keyboard_arrow_down

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

View abstract keyboard_arrow_down

11 results in Authors: Chenru Duan