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Chenru Duan

Showing results (1-10 of 41) with videos related to

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Annual Review of Chemical and Biomolecular Engineering|March 23, 2022
Machine Learning for the Discovery, Design, and Engineering of MaterialsChenru Duan, Aditya Nandy, Heather J Kulik
The Journal of Physical Chemistry Letters|September 1, 2020
Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput ScreeningFang Liu, Chenru Duan, Heather J Kulik
Journal of the American Chemical Society|October 13, 2021
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic FrameworksAditya Nandy, Chenru Duan, Heather J Kulik
The Journal of Physical Chemistry Letters|December 5, 2023
Classification of Hemilabile Ligands Using Machine LearningIlia Kevlishvili, Chenru Duan, Heather J Kulik
The Journal of Chemical Physics|November 4, 2017
The study of an extended hierarchy equation of motion in the spin-boson model: The cutoff function of the sub-Ohmic spectral densityChenru Duan, Qianlong Wang, Zhoufei Tang, et al.
The Journal of Chemical Physics|May 14, 2022
Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximationsIsuru R Ariyarathna, Chenru Duan, Heather J Kulik
Physical Chemistry Chemical Physics : PCCP|March 6, 2023
Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning modelsYael Cytter, Aditya Nandy, Chenru Duan, et al.
Journal of Chemical Theory and Computation|June 16, 2020
Data-Driven Approaches Can Overcome the Cost-Accuracy Trade-Off in Multireference DiagnosticsChenru Duan, Fang Liu, Aditya Nandy, et al.
The Journal of Physical Chemistry Letters|May 11, 2021
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials DiscoveryChenru Duan, Fang Liu, Aditya Nandy, et al.
JACS Au|June 1, 2022
New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million CatalystsAditya Nandy, Chenru Duan, Conrad Goffinet, et al.
Pageof 5

Showing results (1-10 of 41) with videos related to

Sort By:
Pageof 5
Annual Review of Chemical and Biomolecular Engineering|March 23, 2022
Machine Learning for the Discovery, Design, and Engineering of MaterialsChenru Duan, Aditya Nandy, Heather J Kulik
The Journal of Physical Chemistry Letters|September 1, 2020
Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput ScreeningFang Liu, Chenru Duan, Heather J Kulik
Journal of the American Chemical Society|October 13, 2021
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic FrameworksAditya Nandy, Chenru Duan, Heather J Kulik
The Journal of Physical Chemistry Letters|December 5, 2023
Classification of Hemilabile Ligands Using Machine LearningIlia Kevlishvili, Chenru Duan, Heather J Kulik
The Journal of Chemical Physics|November 4, 2017
The study of an extended hierarchy equation of motion in the spin-boson model: The cutoff function of the sub-Ohmic spectral densityChenru Duan, Qianlong Wang, Zhoufei Tang, et al.
The Journal of Chemical Physics|May 14, 2022
Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximationsIsuru R Ariyarathna, Chenru Duan, Heather J Kulik
Physical Chemistry Chemical Physics : PCCP|March 6, 2023
Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning modelsYael Cytter, Aditya Nandy, Chenru Duan, et al.
Journal of Chemical Theory and Computation|June 16, 2020
Data-Driven Approaches Can Overcome the Cost-Accuracy Trade-Off in Multireference DiagnosticsChenru Duan, Fang Liu, Aditya Nandy, et al.
The Journal of Physical Chemistry Letters|May 11, 2021
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials DiscoveryChenru Duan, Fang Liu, Aditya Nandy, et al.
JACS Au|June 1, 2022
New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million CatalystsAditya Nandy, Chenru Duan, Conrad Goffinet, et al.
Pageof 5