Technical Research
Pete's technical research involves the use of advanced modeling algorithms to provide insight into the properties and behavior of synthetic and biological molecular systems. Current projects include:
Accurate Formulation of Coarse-Grained (Mesoscopic) Models for Polymers
Simulation of large polymer systems require significant computational resources that are often beyond those typically available. This work is a collaboration with Dr. Cliff Henderson, Dean of the College of Engineering, and is being carried out by Sahar Zenoozi, who is about to finish her Ph.D. in Chemical Engineering at the University of South Florida. The approach involves a detailed mapping of atomistic simulations to a coarse-grained model, followed by a tuning of the coarse-grained model to accurately reproduce the polymer properties. Recently, tuning of the intrachain force field parameters to reflect the reduced fluctuations in the coarse-grained beads has resulted in the first model to accurately predict properties in both the melt and glassy phases as well as an accurate glass transition.
Rational Design of Co-Solvents for Cannabinoid Extraction
Alabama's progressive medial cannabis law provides the potential to expand the state economy in this rapidly growing medical area. Currently, most producers use various extracts from natural cannabis without regard to optimizing the therapeutic effect of the distribution of the 30 or so cannabinoids that exist in most cannabis plants. We are currently doing advanced simulations to rationally design optimal co-solvents to be added to the supercritical CO2 typically used in the extraction of these cannabinoids. Optimization of these co-solvent extraction methods will allow customized therapeutics to target specific medical pathologies. Randall Scholar, Andrew Navarez is currently working on producing accurate simulations of these systems.
Efficient Hybrid Simulation Algorithms for Viscous Molecular Systems
The most challenging molecular simulations include large molecules like proteins and synthetic polymers that move at time scales many orders of magnitude slower than small molecules. Given this slow movement, we have developed an efficient hybrid simulation algorithm called Protracted Colored Noise Dynamics. It is a hybrid method, part way between deterministic methods like molecular dynamics, and stochastic methods like Langevin dynamics, and it can relax entangled polymer systems six orders of magnitude faster than normal molecular dynamics. We are applying this method to various polymers systems that contain levels of structural order between random amorphous materials and ordered crystalline materials to understand the nuanced effect this level of order has on material properties and behavior.
