Honors & Awards

  • Governor's Medal, Indian Institute of Technology, Madras (07-19-2019)
  • Sri Rajesh Achanta Award, Indian Institute of Technology, Madras (04-22-2019)
  • Institue Blues Merit, Indian Institute of Technology, Madras (04-22-2019)

Professional Education

  • Doctor of Philosophy, Stanford University, ME-PHD (2024)
  • Master of Technology, Indian Institute of Technology, Madras, Mechanical Engineering - Product Design (2019)
  • Bachelor of Technology, Indian Institute of Technology, Madras, Mechanical Engineering (2019)

Stanford Advisors

  • Wei Cai, Postdoctoral Faculty Sponsor

Lab Affiliations

All Publications

  • Network Evolution Controlling Strain-Induced Damage and Self-Healing of Elastomers with Dynamic Bonds MACROMOLECULES Yin, Y., Mohanty, S., Cooper, C. B., Bao, Z., Cai, W. 2024
  • Modeling shortest paths in polymeric networks using spatial branching processes JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS Zhang, Z., Mohanty, S., Blanchet, J., Cai, W. 2024; 187
  • Development of scalable and generalizable machine learned force field for polymers. Scientific reports Mohanty, S., Stevenson, J., Browning, A. R., Jacobson, L., Leswing, K., Halls, M. D., Afzal, M. A. 2023; 13 (1): 17251


    Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain polymer systems such as polyethylene glycol. As a solution, we present an alternative potential energy surface, a charge recursive neural network (QRNN) model trained on DFT calculations made on smaller atomic clusters that generalizes well to oligomers comprising larger atomic clusters or longer chains. We demonstrate the validity of the polymer QRNN workflow by modeling the oligomers of ethylene glycol. We apply two rounds of active learning (addition of new training clusters based on current model performance) and implement a novel model training approach that uses partial charges from a semi-empirical method. Our developed QRNN model for polymers produces stable molecular dynamics (MD) simulation trajectory and captures the dynamics of polymer chains as indicated by the striking agreement with experimental values. Our model allows working on much larger systems than allowed by DFT simulations, at the same time providing a more accurate force field than classical force fields which provides a promising avenue for large-scale molecular simulations of polymeric systems.

    View details for DOI 10.1038/s41598-023-43804-5

    View details for PubMedID 37821501

    View details for PubMedCentralID PMC10567837

  • Evaluating the transferability of machine-learned force fields for material property modeling COMPUTER PHYSICS COMMUNICATIONS Mohanty, S., Yoo, S., Kang, K., Cai, W. 2023; 288
  • High energy density flexible and ecofriendly lithium-ion smart battery ENERGY STORAGE MATERIALS Kuznetsov, O. A., Mohanty, S., Pigos, E., Chen, G., Cai, W., Harutyunyan, A. R. 2023; 54: 266-275
  • Computational approaches to model X-ray photon correlation spectroscopy from molecular dynamics MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING Mohanty, S., Cooper, C. B., Wang, H., Liang, M., Cai, W. 2022; 30 (7)
  • An analytical model for shape morphing through combined bending and twisting in piezo composites MECHANICS OF MATERIALS Boddapati, J., Mohanty, S., Annabattula, R. 2020; 144
  • A phase-field model for crack growth in electro-mechanically coupled functionally graded piezo ceramics SMART MATERIALS AND STRUCTURES Mohanty, S., Kumbhar, P., Swaminathan, N., Annabattula, R. 2020; 29 (4)
  • Stress-electrochemistry interactions in a composite electrode for Li-ion batteries SOLID STATE IONICS Mohanty, S., Kumbhar, P., Annabattula, R., Swaminathan, N. 2019; 342
  • A finite strain based coupled chemo-mechanical study of the anode materials in lithium-ion batteries JOURNAL OF COUPLED SYSTEMS AND MULTISCALE DYNAMICS Mohanty, S., Kumbhar, P., Swaminathan, N., Annabattula, R. 2018; 6 (4): 266-272