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Chenru Duan

Showing results (31-40 of 41) with videos related to

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JACS Au|March 6, 2023
Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible ChromophoresChenru Duan, Aditya Nandy, Gianmarco G Terrones, et al.
Scientific Data|March 12, 2022
MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworksAditya Nandy, Gianmarco Terrones, Naveen Arunachalam, et al.
Journal of the American Chemical Society|July 3, 2025
Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language ModelsJieyu Lu, Zhangde Song, Qiyuan Zhao, et al.
The Journal of Chemical Physics|February 20, 2022
Representations and strategies for transferable machine learning improve model performance in chemical discoveryDaniel R Harper, Aditya Nandy, Naveen Arunachalam, et al.
Chemical Reviews|July 14, 2021
Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine LearningAditya Nandy, Chenru Duan, Michael G Taylor, et al.
Inorganic Chemistry|March 6, 2019
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic ChemistryJon Paul Janet, Fang Liu, Aditya Nandy, et al.
The Journal of Physical Chemistry. A|April 1, 2020
Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure PredictionsMichael G Taylor, Tzuhsiung Yang, Sean Lin, et al.
Physical Chemistry Chemical Physics : PCCP|August 22, 2020
Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energeticsAditya Nandy, Daniel B K Chu, Daniel R Harper, et al.
Journal of Chemical Theory and Computation|July 14, 2022
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex LigandsChenru Duan, Adriana J Ladera, Julian C-L Liu, et al.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)|July 13, 2025
Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative ModelsQiyuan Zhao, Yunhong Han, Duo Zhang, et al.
Pageof 5

Showing results (31-40 of 41) with videos related to

Sort By:
Pageof 5
JACS Au|March 6, 2023
Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible ChromophoresChenru Duan, Aditya Nandy, Gianmarco G Terrones, et al.
Scientific Data|March 12, 2022
MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworksAditya Nandy, Gianmarco Terrones, Naveen Arunachalam, et al.
Journal of the American Chemical Society|July 3, 2025
Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language ModelsJieyu Lu, Zhangde Song, Qiyuan Zhao, et al.
The Journal of Chemical Physics|February 20, 2022
Representations and strategies for transferable machine learning improve model performance in chemical discoveryDaniel R Harper, Aditya Nandy, Naveen Arunachalam, et al.
Chemical Reviews|July 14, 2021
Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine LearningAditya Nandy, Chenru Duan, Michael G Taylor, et al.
Inorganic Chemistry|March 6, 2019
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic ChemistryJon Paul Janet, Fang Liu, Aditya Nandy, et al.
The Journal of Physical Chemistry. A|April 1, 2020
Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure PredictionsMichael G Taylor, Tzuhsiung Yang, Sean Lin, et al.
Physical Chemistry Chemical Physics : PCCP|August 22, 2020
Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energeticsAditya Nandy, Daniel B K Chu, Daniel R Harper, et al.
Journal of Chemical Theory and Computation|July 14, 2022
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex LigandsChenru Duan, Adriana J Ladera, Julian C-L Liu, et al.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)|July 13, 2025
Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative ModelsQiyuan Zhao, Yunhong Han, Duo Zhang, et al.
Pageof 5