Graduate students Shehan Parmar (McDaniel group) and Austin Wallace (Sherrill group) have been awarded fellowships from the Molecular Sciences Software Institute (MolSSI)  to support their work in computational chemistry. MolSSI Fellows receive $40,000 over the course of a year and are paired with a MolSSI staff scientist mentor who oversees their software development efforts and training. They will also participate in a week-long summer institute at MoSSI headquarters at Virginia Tech focusing on best practices for the development and long-term maintenance of software for computational chemistry.

Shehan, a fourth-year PhD candidate, studies a class of materials known as ionic liquids (ILs) which show great promise as alternative electrolytes for energy storage applications due to their unique electrochemical properties and tunability. ILs are molten salts composed of cation/anion pairs. With hundreds of thousands of possible cation/anion combinations, ILs present a vast design space that is prohibitively expensive to explore through experiments alone. Thus, the MolSSI Fellowship Program will support Shehan in developing high-throughput molecular dynamics (HTMD) software to enable large-scale, computational screening of key IL properties—such as conductivity, viscosity, surface tension—using first-principles methods.

Austin is a fourth-year Ph.D. candidate in the Sherrill Group, working at the intersection of quantum chemistry, software development, and machine learning. His research centers on quantum mechanical interaction energies, particularly using symmetry-adapted perturbation theory (SAPT) to acquire component energies in terms of fundamental forces: electrostatics, exchange-repulsion, induction/polarization, and dispersion. In previous work, the Sherrill group has evaluated SAPT across multiple levels of theory and explored cost reductions by replacing quantum dispersion energies with empirical -D3/-D4 models. These studies have informed the selection of target methods for training next-generation atom-pairwise neural networks.

As a MolSSI Software Fellow, he will focus on consolidating the code in Psi4 to improve performance and readability, while extending a valuable functional group partitioning scheme to SAPT(DFT) energies. This enhanced method will readily be applied in generating energy labels for protein–ligand systems, improving data efficiency and pairwise energy accuracy in training a new model. Furthermore, the fellowship will also provide guidance on improving his software packages.

You can read more about the MolSSI Fellowship Program and the 2025 fellows here.