A team of researchers from Wayne State University's College of Engineering recently received nearly $500,000 from the National Science Foundation for its research project, SSE: Development of a High-Performance Parallel Gibbs Ensemble Monte Carlo Simulation Engine.
"Molecular dynamics codes that utilize parallel computation on CPUs and GPUs [graphics processing units] are relatively well developed; however, there are a number of problems that cannot be simulated with this methodology," said Jeffrey Potoff, Ph.D., co-principal investigator of the project and associate dean for academic and student affairs and professor of chemical engineering in the College of Engineering. "Problems that require the simulation of an open system, such as adsorption in porous materials, require an alternative methodology that allows for fluctuation in the number of molecules in the system. There are a number of systems where the presence of large free-energy barriers and slow diffusion preclude the use of standard molecular dynamics."
Examples of such problems requiring alternative methods include prediction of phase equilibria in multicomponent lipid bilayers, polymers or ionic liquids. For these types of problems, Monte Carlo or hybrid Monte Carlo/molecular dynamics simulations have the potential to significantly improve computational efficiency.
According to Loren Schwiebert, Ph.D., professor and chair of computer science in Wayne State's College of Engineering, the project is focused on the development of the open-source Monte Carlo simulation engine known as the GPU Optimized Monte Carlo - or GOMC - which is able to use low-cost graphics processing units and CPUs to significantly reduce computational time.
"Our research will enable Monte Carlo simulations to be performed with higher fidelity in larger systems than is currently accessible with standard Monte Carlo simulation codes, enabling the accelerated development of new materials," said Schwiebert. "The project also will benefit graduate and undergraduate students by providing them training in Monte Carlo simulation, design of efficient algorithms for parallel computation on a variety of hardware architectures, and software development."
The grant number for this National Science Foundation award is 1642406.