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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

773

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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

Version 1 posted 07 May 2020

1,040

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1

Citations

Theoretical and Computational Chemistry

Working Paper

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

Chenru Duan, Fang Liu, Aditya Nandy, Heather Kulik

Version 1 posted 13 April 2020

969

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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

828

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1

Citations

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

Version 1 posted 02 July 2020

853

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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

1,770

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1

Citations

Theoretical and Computational Chemistry

Working Paper

Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

Aditya Nandy, Heather Kulik

Version 1 posted 05 October 2020

667

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Theoretical and Computational Chemistry

Working Paper

The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

Vyshnavi Vennelakanti, Aditya Nandy, Heather Kulik

Version 1 posted 11 June 2021

424

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Materials Chemistry

Working Paper

DFT-based Multireference Diagnostics in the Solid State: Focusing on Metal-organic Frameworks

Yeongsu Cho, Aditya Nandy, Chenru Duan, Heather Kulik

Version 1 posted 29 August 2022

430

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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

1,356

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Refine Search

25 results in Aditya Nandy in Authors: Aditya Nandy

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

Understanding the Diversity of the Metal-Organic Framework Ecosystem

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

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

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

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

Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

DFT-based Multireference Diagnostics in the Solid State: Focusing on Metal-organic Frameworks

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

25 results in Authors: Aditya Nandy