Shehan Parmar, a Ph.D. candidate in chemistry at Georgia Tech and a 2025–26 MolSSI Software Fellow, builds simulation and software infrastructure for ionic liquid discovery, a class of liquids whose tunable chemistry makes them candidates for spacecraft propellants, battery electrolytes, and energy-efficient desalination. Under the guidance of Prof. Jesse McDaniel, with prior support from the DOE Computational Science Graduate Fellowship, he develops the polarizable force fields, software, and AI/ML pipelines needed to let engineers select across an otherwise impossibly large space of candidate chemistries.
In some ways, Shehan came into molecular science by coincidence. He earned a B.S. in Astronautical Engineering at USC and an M.S. in Aerospace Engineering at UCLA, where his first research project was on how propellants behave under high-magnitude electric fields, an experimental electrospray thruster problem funded by the Air Force Research Lab and NASA JPL. His fascination with space quickly turned into a realization: to understand propellants, one must understand chemistry. That’s when his passion grew for understanding and answering questions through first principles. In 2022 he switched universities and majors mid-PhD to do exactly that, joining Prof. Jesse McDaniel’s group at Georgia Tech.
At Georgia Tech, Shehan’s research has anchored on the molecular behavior of ionic liquid mixtures: their structure, their transport properties, their phase behavior. His published work includes an Editor’s Choice paper on HEHN-based ionic liquid mixtures (J. Phys. Chem. B 2023) and a collaboration with Oak Ridge National Lab on a tetraalkylammonium TFSI ionic liquid (J. Phys. Chem. B 2024).
Through the MolSSI Software Fellowship, Shehan has been bringing two historically separate domains together: ionic liquid science and high-throughput materials discovery. Working with his mentor, Dr. Samuel Ellis, he has been integrating libraries across disciplines and following best software practices to package and deploy reproducible, provenance-aware, and modular high-throughput workflows. One outcome is pympfit, his open-source multipole-based partial-charge fitting library, now refactored as a modular package collaborators can extend. The fellowship complements his previous role as a visiting graduate student in Prof. Kristin Persson‘s group at Lawrence Berkeley National Lab, where he contributed a small feature to atomate2, the Materials Project’s JobFlow- and FireWorks-based workflow framework, helping route classical MD jobs for ionic liquids and electrolytes into dataset-generation pipelines that will eventually feed into machine learning force fields. It is the kind of cross-domain work MolSSI has uniquely supported.
Beyond graduate school, Shehan is eager to push the frontier of materials discovery and to explore how far simulation and AI can go in expanding the chemistries engineers can draw from when designing the next generation of materials. Some of his most fascinating moments in grad school have been the times his molecular dynamics simulations matched independent experimental measurements. Working with talented experimental collaborators to compute properties that turn out to agree, sometimes strikingly well, with what the lab measures has been the clearest signal he could ask for about how promising computational chemistry really is. Whether in industry, a startup, or an academic role, he hopes to keep solving these kinds of problems.
Outside the lab, Shehan teaches yoga in Midtown, Atlanta, trains at his CrossFit gym Team Octopus, and listens to Better by Khalid on repeat for advanced programming focus.
If you’d like to follow Shehan’s work, you can find him on GitHub, LinkedIn, Google Scholar, and his personal website.
