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Siddarth K Achar

Showing results (1-10 of 8) with videos related to

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Chemical Reviews|December 25, 2024
Small Data Machine Learning Approaches in Molecular and Materials ScienceSiddarth K Achar, John A Keith
Nanomaterials (Basel, Switzerland)|June 27, 2023
Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic PositionsSiddarth K Achar, Leonardo Bernasconi, J Karl Johnson
ACS Applied Materials & Interfaces|December 14, 2022
Using Machine Learning Potentials to Explore Interdiffusion at Metal-Chalcogenide InterfacesSiddarth K Achar, Julian Schneider, Derek A Stewart
The Journal of Physical Chemistry. C, Nanomaterials and Interfaces|December 4, 2025
Proton Transport on Graphamine: A Deep-Learning Potential StudyLakshmi Y Ananthabhotla, Siddarth K Achar, J Karl Johnson
Journal of Chemical Theory and Computation|June 2, 2022
Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66Siddarth K Achar, Jacob J Wardzala, Leonardo Bernasconi, et al.
ACS Applied Materials & Interfaces|May 16, 2023
In Silico Demonstration of Fast Anhydrous Proton Conduction on GraphanolSiddarth K Achar, Leonardo Bernasconi, Ruby I DeMaio, et al.
Angewandte Chemie (International Ed. in English)|July 24, 2024
Leveraging Ligand Steric Demand to Control Ligand Exchange and Domain Composition in Stratified Metal-Organic FrameworksYiwen He, Mattheus De Souza, Tian-Yi Luo, et al.
Journal of Chemical Theory and Computation|September 3, 2025
Reactive Active Learning: An Efficient Approach for Training Machine Learning Interatomic Potentials for Reacting SystemsSiddarth K Achar, Priyanka B Shukla, Chinmay V Mhatre, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Chemical Reviews|December 25, 2024
Small Data Machine Learning Approaches in Molecular and Materials ScienceSiddarth K Achar, John A Keith
Nanomaterials (Basel, Switzerland)|June 27, 2023
Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic PositionsSiddarth K Achar, Leonardo Bernasconi, J Karl Johnson
ACS Applied Materials & Interfaces|December 14, 2022
Using Machine Learning Potentials to Explore Interdiffusion at Metal-Chalcogenide InterfacesSiddarth K Achar, Julian Schneider, Derek A Stewart
The Journal of Physical Chemistry. C, Nanomaterials and Interfaces|December 4, 2025
Proton Transport on Graphamine: A Deep-Learning Potential StudyLakshmi Y Ananthabhotla, Siddarth K Achar, J Karl Johnson
Journal of Chemical Theory and Computation|June 2, 2022
Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66Siddarth K Achar, Jacob J Wardzala, Leonardo Bernasconi, et al.
ACS Applied Materials & Interfaces|May 16, 2023
In Silico Demonstration of Fast Anhydrous Proton Conduction on GraphanolSiddarth K Achar, Leonardo Bernasconi, Ruby I DeMaio, et al.
Angewandte Chemie (International Ed. in English)|July 24, 2024
Leveraging Ligand Steric Demand to Control Ligand Exchange and Domain Composition in Stratified Metal-Organic FrameworksYiwen He, Mattheus De Souza, Tian-Yi Luo, et al.
Journal of Chemical Theory and Computation|September 3, 2025
Reactive Active Learning: An Efficient Approach for Training Machine Learning Interatomic Potentials for Reacting SystemsSiddarth K Achar, Priyanka B Shukla, Chinmay V Mhatre, et al.
Pageof 1