We are a statistical mechanics research group at the University of Minnesota specializing in particle-based computer simulation. Our research efforts are driven by the desire to learn about complex chemical systems on the microscopic, or molecular, level. We focus on understanding how molecular architecture and composition influence structure, phase behavior and function of the system of interest. The challenge of molecular simulation is the ability to make thermodynamic predictions that are both accurate and precise. Accuracy is dependent upon a correct description of molecular interactions in the system, modeled using a force field. Precision is dependent on the degree to which relevant (highly-probable) configurations of the system are sampled. In answering this challenge, our group has developed transferable force fields (TraPPE-UA, TraPPE-EH, TraPPE-pol) that more reasonably model the potential energy of interacting molecules, particularly for the calculation of phase equilibria, and novel simulation algorithms, such as configurational-bias Monte Carlo (CBMC) and aggregation-volume-bias Monte Carlo (AVBMC), for improving the ability to sample important configurations of the system. Our Monte Carlo search algorithms and the Transferable Potentials for Phase Equilibria (TraPPE) force field allow us to predict both accurate and precise thermodynamic data for a wide range of chemical systems, including systems which are not amenable to conventional simulation approaches and for which experimental data are not readily accessible. Please visit our research page to learn more!
Zhengyuan (Don) Shen receives the 2021-2022 Richard D. Amelar and Arthur S. Lodge Fellowship for Outstanding Collaborative Research in Materials. This award is given annually to a graduate student from the Departments of Chemistry or Chemical Engineering and Materials Science who has demonstrated excellence in their area of interest and a willingness to collaborate with other students and/or groups.