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The Journal of Chemical Physics
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February 20, 2022
Representations and strategies for transferable machine learning improve model performance in chemical discovery
Daniel 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 Learning
Aditya Nandy, Chenru Duan, Michael G Taylor, et al.
Journal of Chemical Information and Modeling
|
November 28, 2024
Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery
Daniel B K Chu, David A González-Narváez, Ralf Meyer, et al.
Inorganic Chemistry
|
March 6, 2019
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
Jon 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 Predictions
Michael 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 energetics
Aditya Nandy, Daniel B K Chu, Daniel R Harper, et al.
Journal of Biomechanics
|
December 24, 2017
A modular approach to creating large engineered cartilage surfaces
Audrey C Ford, Wan Fung Chui, Anne Y Zeng, et al.
Chemical Science
|
August 30, 2018
Highly effective ammonia removal in a series of Brønsted acidic porous polymers: investigation of chemical and structural variations
Gokhan Barin, Gregory W Peterson, Valentina Crocellà, et al.
Nature Communications
|
August 15, 2020
Understanding the diversity of the metal-organic framework ecosystem
Seyed Mohamad Moosavi, Aditya Nandy, Kevin Maik Jablonka, et al.
The Journal of Chemical Physics
|
November 15, 2022
Ligand additivity relationships enable efficient exploration of transition metal chemical space
Naveen Arunachalam, Stefan Gugler, Michael G Taylor, et al.
Page
of 5
Search research articles
Search
Showing results (31-40 of 42) with videos related to
Sort By:
Page
of 5
The Journal of Chemical Physics
|
February 20, 2022
Representations and strategies for transferable machine learning improve model performance in chemical discovery
Daniel 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 Learning
Aditya Nandy, Chenru Duan, Michael G Taylor, et al.
Journal of Chemical Information and Modeling
|
November 28, 2024
Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery
Daniel B K Chu, David A González-Narváez, Ralf Meyer, et al.
Inorganic Chemistry
|
March 6, 2019
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
Jon 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 Predictions
Michael 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 energetics
Aditya Nandy, Daniel B K Chu, Daniel R Harper, et al.
Journal of Biomechanics
|
December 24, 2017
A modular approach to creating large engineered cartilage surfaces
Audrey C Ford, Wan Fung Chui, Anne Y Zeng, et al.
Chemical Science
|
August 30, 2018
Highly effective ammonia removal in a series of Brønsted acidic porous polymers: investigation of chemical and structural variations
Gokhan Barin, Gregory W Peterson, Valentina Crocellà, et al.
Nature Communications
|
August 15, 2020
Understanding the diversity of the metal-organic framework ecosystem
Seyed Mohamad Moosavi, Aditya Nandy, Kevin Maik Jablonka, et al.
The Journal of Chemical Physics
|
November 15, 2022
Ligand additivity relationships enable efficient exploration of transition metal chemical space
Naveen Arunachalam, Stefan Gugler, Michael G Taylor, et al.
Page
of 5