MS, Carnegie Mellon University, 2018 - 2019
BE, R.V. College of Engineering, 2014 - 2018
Achar, S.K., Bernasconi, L., & Johnson, J.K. (2023). Machine Learning Electron Density Prediction Using Weighted Smooth Overlap of Atomic Positions. Nanomaterials (Basel), 13(12), 1853.MDPI. doi: 10.3390/nano13121853.
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, 38(24), 5114-5124.Springer Nature. doi: 10.1557/s43578-023-01141-3.
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 Appl Mater Interfaces, 15(21), 25873-25883.American Chemical Society (ACS). doi: 10.1021/acsami.3c04022.
Achar, S.K., Schneider, J., & Stewart, D.A. (2022). Using Machine Learning Potentials to Explore Interdiffusion at Metal-Chalcogenide Interfaces. ACS Appl Mater Interfaces, 14(51), 56963-56974.American Chemical Society (ACS). doi: 10.1021/acsami.2c16254.
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. J Chem Theory Comput, 18(6), 3593-3606.American Chemical Society (ACS). doi: 10.1021/acs.jctc.2c00010.
Yang, Y., Achar, S.K., & Kitchin, J.R. (2022). Evaluation of the degree of rate control via automatic differentiation. AIChE Journal, 68(6).Wiley. doi: 10.1002/aic.17653.
Achar, S.K., Zhang, L., & Johnson, J.K. (2021). Efficiently Trained Deep Learning Potential for Graphane. JOURNAL OF PHYSICAL CHEMISTRY C, 125(27), 14874-14882.American Chemical Society (ACS). doi: 10.1021/acs.jpcc.1c01411.
Achar, S., & Johnson, J.K. (2020). Towards a deep learning potential for anhydrous proton transport. In AIChE Annual Meeting, Conference Proceedings, 2020-November.
Gupta, S., Bonageri, S., Achar, S.K., Menon, A., & Basavaraja, R.J. (2018). Synthesis of porous graphene powder through improved Hummers’ method. In AIP Conference Proceedings, 1966(1), (p. 020014).AIP Publishing. doi: 10.1063/1.5038693.