Committee Chair

Barisik, Murat

Committee Member

Sreenivas, Kidambi; Ranjan, Reetesh

Department

Dept. of Mechanical Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

Water plays a critical role in thermal transport, electrochemical energy storage, and electrically driven interfacial phenomena. Accurately capturing its coupled thermo-electrical behavior remains a challenge in MD simulations. This study combines machine learning, and MD simulations to provide a computational study of water and electrolyte systems. To increase the predictive accuracy of the TIP4P water model, a machine learning-guided reparameterization has been developed which shows better concordance with experimental dielectric and thermal properties of water. Using this model, the electro-thermal behavior of water under external electric fields has been investigated. A concentration-driven shift from field-responsive transport to structurally arrested dynamics dominated by ion pairing has been explored further using this model as a solvent in nonequilibrium MD simulations of NaCl and NaClO4 electrolytes. Altogether, this work offers molecular-level under-standing of electro-thermal transport phenomena and creates a foundation for modeling water and electrolyte system for energy device.

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. CBET 2347562 and partially supported by the FY2025-28 Center of Excellence for Applied Computational Science competition. I am grateful to my advisor, Dr. Murat Barisik, who has offered valuable guidance, support, and motivation throughout my graduate studies. His insight and intuition have shaped this work’s depth and scope. I would like to thank my teachers, friends, and colleagues in the Mechanical Engineering Department for their encouraging discussions and motivation. I am grateful to the support from the UTC Information Technology HPC Team for providing computing facilities. I would like to offer my heartfelt thanks to my family, Pradip Narayan Dey, Shikha Rani Dey, Dr. Upama Dey, Pushpita Dey, and Dr. Arup Dhar for their unwavering encouragement and love. I also wish to extend my heartfelt thanks to my friends Mahadi Hasan, Rajon Dey, Anindita Chowdhury, Rudroraj Dey, Nadia Binte Asif, Jan E Alam Sajib, Atal Bhowmik, Ashfak Md Shibli, Fatema Akter Bely, Mithu Chanda, Kironmoy Paul Shourov, Priya Chowdhury, Md Shahidul Islam, Mehedi Hasan, Ratri Chowdhury, Nishat Tasnim, Nahin Rahman, Fatin Ihsan, Samuchsash Swargo, Sam Burgess, Cathy Burgess, Gary Reid, Dr. Rajesh Ramesh, Talha Ahmed and many others for their support, companionship and encouragement which have been a constant source of comfort and motivation throughout my master’s journey.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

8-2026

Subject

Energy storage; Supercapacitors; Molecules--Models; Molecular dynamics; Water as fuel

Keyword

Molecular Modeling; Machine Learning; Na-ion Electrolyte; EDL Supercapacitor; Electrothermal Coupling; Energy Storage

Document Type

Masters theses

DCMI Type

Text

Extent

xiii, 91 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

Date Available

8-1-2027

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Available for download on Sunday, August 01, 2027

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