Ananthabhotla, L.Y., Achar, S. K., Johnson, K. J. (2025). Proton Transport on Graphamine: A Deep-Learning Potential Study. The Journal of Physical Chemistry C. Link
Achar, S. K., Shukla, P. B., Mhatre, C. V., Vinger, C. Y., Johnson, K. J. (2025). Reactive Active Learning: An Efficient Approach for Training Machine Learning Interatomic Potentials for Reacting Systems. Journal of Chemical Theory and Computation. Link
Achar, S. K., Keith, J. A. (2024). Small Data Machine Learning Approaches in Molecular and Materials Science. Chemical Reviews, 124(24), 13571–13573. Link
He, Y., De Souza, M., Luo, T., Achar, S. K., Johnson, K. J., Rosi, N. L. (2024). Leveraging Ligand Steric Demand to Control Ligand Exchange and Domain Composition in Stratified Metal–Organic Frameworks. Angewandte Chemie International Edition. Link
Achar, S. K., Bernasconi, L., Alvarez, J. J., Johnson, J. K. (2023). Deep-Learning Potentials for Proton Transport in Double-Sided Graphanol. Journal of Materials Research, 1–11. Link
Achar, S. K., Bernasconi, L., Johnson, J. K. (2023). Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic Positions. Nanomaterials, 13(12), 1853. (Editor’s Choice). Link
Achar, S. K., Bernasconi, L., DeMaio, R. I., Howard, K. R., Johnson, J. K. (2023). In Silico Demonstration of Fast Anhydrous Proton Conduction on Graphanol. ACS Applied Materials & Interfaces, 15(21), 25873–25883. Link
Achar, S. K., Stewart, D., Schneider, J. (2022). Using Machine Learning Potentials to Explore Interdiffusion at Metal–Chalcogenide Interfaces. ACS Applied Materials & Interfaces. Link
Achar, S. K., Wardzala, J. J., Bernasconi, L., Zhang, L., Johnson, J. K. (2022). Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66. Journal of Chemical Theory and Computation, 18, 3593–3606. Link
Yang, Y., Achar, S. K., Kitchin, J. R. (2022). Evaluation of the Degree of Rate Control via Automatic Differentiation. AIChE Journal, 68(6), e17653. Link
Achar, S. K., Zhang, L., Johnson, J. K. (2021). Efficiently Trained Deep Learning Potential for Graphane. The Journal of Physical Chemistry C, 125(27), 14874–14882. Link
Madathil, A. P., Achar, S. K., Moses, V., Meda, U. S., Chetan, N., Vidya, C., Sarode, M. (2020). Use of Keratin Present in Chicken Feather as a Hydrogen Storage Material: A Review. International Journal of Engineering Materials and Manufacture, 5(4), 148–155. Link
Achar, S. K., Madathil, A. P., Naveen, C., Gosh, B., Phani, A. R. (2018). Thickness-Dependent Optical Properties of Sol–Gel Based MgF₂–TiO₂ Thin Films. Journal of Mechanics, Materials Science & Engineering, 179(52), 2412–5954. Link
Gupta, S., Bonageri, S., Achar, S. K., Menon, A. (2018). Synthesis of Porous Graphene Powder Through Improved Hummers’ Method. AIP Conference Proceedings, 1966(1), 020014. Link
Achar, S. K., Bernasconi, L., Johnson, K. J. (2025). Identifying Proton-Coupled Electron Transfer Using Machine Learning Electron Densities. In preparation.
Achar, S. K., Zadeh, A. S., Ferguson, A. L. (2025). Gentlest Ascent Dynamics for Enhanced Sampling and Free Energy Landscape Exploration. In preparation.
Achar, S. K., Ferguson, A. L. (2025). Bayesian Optimization for Designing Sensitive and Selective Cyclodextrin-Based PFAS Probes. In preparation.