College of Science & Engineering
The Siepmann group develops a variety of computational chemistry tools including: Monte Carlo algorithms for efficient sampling of macromolecular conformations and spatial distributions in multi-component multi-phase systems; accurate and transferable force fields with multiple levels of resolution; first principles simulation approaches; high-throughput simulation and machine learning approaches for the discovery of functional materials; and large-scale molecular simulations to investigate thermodynamic and transport properties relevant to turbulent multi-phase flows of aqueous systems. The group investigates phase, sorption, and chemical equilibria, self-aggregation behavior and partitioning in polar and non-polar bulk fluids and in heterogeneous and interfacial systems.
In particle, the Siepmann group's efforts are directed to:
- High-throughput screening of nanoporous materials for energy applications
- Understanding chromatographic retention processes including various forms of liquid chromatography and size exclusion chromatography
- Understanding the solvation mechanisms in liquid-liquid and supercritical extraction systems and in surfactant solutions
- Bubble nucleation and multi-phase flow
- Predicting reactive phase equilibria using first principles simulations
The Siepmann group develops its own software for Monte Carlo simulations and utilizes various open-source software for molecular dynamics simulations. On MSI infrastructure, some of these applications use a parallelization hierarchy where large-scale distribution (say, 12 independent trajectories at 16 different state points/compositions) of small, but long runs (1 to 12 cores for 24 hours) are employed, whereas first principles simulations can efficiently utilize 256 to 8,192 cores. Some of the MC/MD software benefits from GPU acceleration.
Research from this group was featured on the MSI website in: