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Theoretical and Computational Chemistry

Working Paper

Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Michael Taylor, Tzuhsiung Yang, Sean Lin, Aditya Nandy, Jon Paul Janet, Chenru Duan, Heather Kulik

728

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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

818

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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

1,722

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1

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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

1,047

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1

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Theoretical and Computational Chemistry

Working Paper

MF-PCBA: Multi-fidelity high-throughput screening benchmarks for drug discovery and machine learning

David Buterez, Jon Paul Janet, Steven J. Kiddle, Pietro Liò

436

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Inorganic Chemistry

Working Paper

Understanding the Diversity of the Metal-Organic Framework Ecosystem

Seyed Mohamad Moosavi, Aditya Nandy, Kevin Maik Jablonka, Daniele Ongari, Jon Paul Janet, Peter G. Boyd, Yongjin Lee, Berend Smit, Heather Kulik

943

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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

1,244

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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

791

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1

Citations

Theoretical and Computational Chemistry

Working Paper

Modelling local and general quantum mechanical properties with attention-based pooling

David Buterez, Jon Paul Janet, Steven J. Kiddle, Dino Oglic, Pietro Liò

150

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Theoretical and Computational Chemistry

Working Paper

Improving De Novo Molecular Design with Curriculum Learning

Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist, Atanas Patronov

1,114

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17 results in Jon Paul Janet in Authors: Jon Paul Janet

Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

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

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

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

MF-PCBA: Multi-fidelity high-throughput screening benchmarks for drug discovery and machine learning

Understanding the Diversity of the Metal-Organic Framework Ecosystem

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

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

Modelling local and general quantum mechanical properties with attention-based pooling

Improving De Novo Molecular Design with Curriculum Learning

17 results in Authors: Jon Paul Janet