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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

Version 1 posted 07 May 2020

1,029

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1

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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

Version 1 posted 28 May 2019

821

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1

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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

Version 2 posted 15 May 2019

1,763

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1

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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

Version 1 posted 23 January 2019

1,336

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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

Version 1 posted 21 February 2020

763

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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

Version 1 posted 21 August 2018

1,103

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1

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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

Version 2 posted 14 February 2022

1,409

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Theoretical and Computational Chemistry

Working Paper

Enumeration of de novo Inorganic Complexes for Chemical Discovery and Machine Learning

Stefan Gugler, Jon Paul Janet, Heather Kulik

Version 1 posted 17 June 2019

727

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Theoretical and Computational Chemistry

Working Paper

DockStream: A Docking Wrapper to Enhance De Novo Molecular Design

Jeff Guo, Jon Paul Janet, Matthias R. Bauer, Eva Nittinger, Kathryn A. Giblin, Kostas Papadopoulos, Alexey Voronov, Atanas Patronov, Ola Engkvist, Christian Margreitter

Version 1 posted 04 August 2021

892

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Theoretical and Computational Chemistry

Working Paper

Link-INVENT: Generative Linker Design with Reinforcement Learning

Jeff Guo, Franziska Knuth, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist, Atanas Patronov

Version 1 posted 25 April 2022

3,640

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5

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22 results in Jon Paul Janet in Authors: Jon Paul Janet

Understanding the Diversity of the Metal-Organic Framework Ecosystem

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

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

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

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

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

Improving De Novo Molecular Design with Curriculum Learning

Enumeration of de novo Inorganic Complexes for Chemical Discovery and Machine Learning

DockStream: A Docking Wrapper to Enhance De Novo Molecular Design

Link-INVENT: Generative Linker Design with Reinforcement Learning

22 results in Authors: Jon Paul Janet