Siddarth Achar
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    • Leveraging Ligand Steric Demand to Control Ligand Exchange and Domain Composition in Stratified Metal–Organic Frameworks
    • Deep-learning potentials for proton transport in double-sided graphanol
    • Deep-learning potentials for proton transport in double-sided graphanol
    • Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic Positions
    • In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol
    • Deep-Learning Potentials for Proton Transport in Double-Sided Graphanol
    • Fast Proton Transport on Graphanol: Mechanistic Insights from Machine Learning and Lattice Models
    • In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol
    • Using Machine Learning Potentials to Explore Interdiffusion at Metal–Chalcogenide Interfaces
    • Using Machine Learning Potentials to Explore Interdiffusion at Metal–Chalcogenide Interfaces
    • Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66
    • Evaluation of the degree of rate control via automatic differentiation
    • Efficiently Trained Deep Learning Potential for Graphane
    • Efficiently Trained Deep Learning Potential for Graphane
    • Use of Keratin Present in Chicken Feather as a Hydrogen Storage Material: A Review
    • Synthesis of porous graphene powder through improved Hummers’ method
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Leveraging Ligand Steric Demand to Control Ligand Exchange and Domain Composition in Stratified Metal–Organic Frameworks

Sep 1, 2024·
Yiwen He
,
Mattheus De Souza
,
Tian‐Yi Luo
,
Siddarth K. Achar
,
J. Karl Johnson
,
Nathaniel L. Rosi
· 0 min read
Cite DOI URL
Type
Journal article
Publication
Angewandte Chemie
Last updated on Sep 1, 2024
Deep-learning potentials for proton transport in double-sided graphanol Aug 1, 2023 →

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