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

Contact

  • Academic
    University - Scholar Department: Mechanical Engineering Position: Postdoctoral Scholar

Additional Info

Links

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

    View details for DOI 10.1021/acs.macromol.4c00409

    View details for Web of Science ID 001250624800001

  • 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

    View details for DOI 10.1016/j.jmps.2024.105636

    View details for Web of Science ID 001231186600001

  • 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

    Abstract

    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

    View details for DOI 10.1016/j.cpc.2023.108723

    View details for Web of Science ID 001122393100001

  • 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

    View details for DOI 10.1016/j.ensm.2022.10.023

    View details for Web of Science ID 000878745500004

  • 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)

    View details for DOI 10.1088/1361-651X/ac860c

    View details for Web of Science ID 000849939600001

  • UNDERSTANDING URBAN WATER CONSUMPTION USING REMOTELY SENSED DATA Mohanty, S., Vijay, A., Deshpande, S., IEEE IEEE. 2022: 2769-2772

    View details for DOI 10.1109/IGARSS46834.2022.9883890

    View details for Web of Science ID 000920916602241

  • 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

    View details for DOI 10.1016/j.mechmat.2020.103350

    View details for Web of Science ID 000527285500014

  • 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)

    View details for DOI 10.1088/1361-665X/ab7145

    View details for Web of Science ID 000537728100005

  • Stress-electrochemistry interactions in a composite electrode for Li-ion batteries SOLID STATE IONICS Mohanty, S., Kumbhar, P., Annabattula, R., Swaminathan, N. 2019; 342

    View details for DOI 10.1016/j.ssi.2019.115053

    View details for Web of Science ID 000503909500008

  • 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

    View details for DOI 10.1166/jcsmd.2018.1169

    View details for Web of Science ID 000468548100004

https://orcid.org/0000-0001-6336-0001