In the Monroe Molecular Simulation Group, we focus on merging machine learning and statistical mechanics on a fundamental level to enhance molecular simulation. The methods we develop enable the rigorous prediction of thermodynamic properties of fluid mixtures and interfaces, in particular those involving self-assembling and adsorbing biomolecules.
Bio-based, and, in particular, protein-based soft materials are increasingly relevant as we transition towards renewable energy sources and sustainable feedstocks that reduce environmental impact. Novel biomolecules and man-made polymers with unique self-assembly behaviors hold promise across many industries, from consumer products to medical devices and biopharmaceuticals. Our research critically links thermodynamic behaviors of such soft materials to their interplay with surrounding water. To yield meaningful predictions of physical properties, especially across the wide ranges of state conditions needed for engineering environmentally responsive behaviors, water must be accurately modeled. While this currently represents a significant challenge, we are working to make computational design of soft materials, especially biomaterials, a reality through three main research areas
- VAE-based multiscale simulation
- Advanced solvation free energy calculations
- Computational design of membrane and chromatographic interfaces
To learn more about specific projects in these areas, please visit our research page.