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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In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol

Mar 1, 2023·
Siddarth Achar
,
Leonardo Bernasconi
,
Ruby DeMaio
,
Katlyn Howard
,
J. Karl Johnson
· 0 min read
Cite DOI URL
Type
Journal article
Publication
American Chemical Society (ACS)
Last updated on Mar 1, 2023
← Fast Proton Transport on Graphanol: Mechanistic Insights from Machine Learning and Lattice Models Mar 1, 2023
Using Machine Learning Potentials to Explore Interdiffusion at Metal–Chalcogenide Interfaces Dec 1, 2022 →

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