In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol von Achar, Siddarth K., Bernasconi, Leonardo, DeMaio, Ruby I., Howard, Katlyn R., Johnson, J. Karl

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Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic Positions von Achar, Siddarth K, Bernasconi, Leonardo, Johnson, J Karl

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Efficiently Trained Deep Learning Potential for Graphane von Achar, Siddarth K, Zhang, Linfeng, Johnson, J. Karl

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Using Machine Learning Potentials to Explore Interdiffusion at Metal–Chalcogenide Interfaces von Achar, Siddarth K., Schneider, Julian, Stewart, Derek A.

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Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66 von Achar, Siddarth K., Wardzala, Jacob J., Bernasconi, Leonardo, Zhang, Linfeng, Johnson, J. Karl

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Evaluation of the degree of rate control via automatic differentiation von Yang, Yilin, Achar, Siddarth K., Kitchin, John R.

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Deep-learning potentials for proton transport in double-sided graphanol von Achar, Siddarth K., Bernasconi, Leonardo, Alvarez, Juan J., Johnson, J. Karl

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Leveraging Ligand Steric Demand to Control Ligand Exchange and Domain Composition in Stratified Metal–Organic Frameworks von He, Yiwen, De Souza, Mattheus, Luo, Tian‐Yi, Achar, Siddarth K., Johnson, J. Karl, Rosi, Nathaniel L.

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Leveraging Ligand Steric Demand to Control Ligand Exchange and Domain Composition in Stratified Metal–Organic Frameworks von He, Yiwen, De Souza, Mattheus, Luo, Tian‐Yi, Achar, Siddarth K., Johnson, J. Karl, Rosi, Nathaniel L.

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