Mohsen Farshad

I am a theoretical and computational physical chemist specializing in advanced computational models, from classical simulations to ab initio methods. My work bridges theory and application, providing insights into complex biological, physical, and chemical systems, with a focus on biophysics, biomolecular engineering, and physicochemical research. Utilizing my background in statistical thermodynamics, quantum mechanics, and kinetics, I combine simulation, theoretical models, and machine learning to solve real-world challenges—from drug discovery and material property predictions to understanding phenomena at a foundational level through systematic, mechanistic interrogation, such as studying the emergent macroscopic phase behavior of a system of interest through collective microscopic and local changes. I integrate machine learning across my work and believe it plays a key role in advancing science. Passionate about interdisciplinary collaboration, I am dedicated to pushing scientific boundaries and delivering impactful solutions. My current research at Duke is on the self-assembly of polymer-grafted nanoparticles and DNA origami. These areas are tightly connected through analysis of how specific interactions drive assembly shape and phase behavior. I use coarse-grained modeling and enhanced sampling to investigate these systems, with a key thrust on developing machine-learning coarse-grained force fields.