Jeffrey Potoff
Wayne State University, MI·Chemistry
Computer simulations let scientists connect how individual molecules interact with the properties of a material. That knowledge can guide the design of new materials tailored to a specific problem.
- Fields
Chemistry
Engineering
Computer Science
- Topics
molecular simulation
molecular dynamics
Monte Carlo
- Grants
- 1 known grant, through Aug 2027
What they work on
- Speed up py-MCMD, the tool that links the molecular dynamics program NAMD with the Monte Carlo program GOMC, by cutting the delay in each MC/MD cycle through improved memory and disk usage.
- Add multi-scale simulation to py-MCMD so the level of molecular detail can change on the fly during a run, using the Nested Monte Carlo Chain approach.
- Revise configurational-bias Monte Carlo algorithms, which build molecules into favorable positions step by step, so they perform well on many-core computer architectures.
- Release the open-source software and Python workflows on GitHub, with YouTube video tutorials, so new users can run hybrid MC/MD simulations.
More about the research
A simulation is only as useful as two things: its models of molecular interactions, and its ability to sample, or explore, enough of the possible molecular arrangements. This project tackles the sampling problem by combining two widely used methods in a single simulation. Molecular dynamics (MD) follows how molecules move over time. Monte Carlo (MC) tries random changes to the system and keeps or rejects them. Using the strengths of each helps a simulation get past barriers that limit either method alone.
Why it matters
Researchers who rely on molecular simulation, for example in chemistry, could get more reliable predictions of material properties. They would gain a hybrid MC/MD package that is fast, rigorously validated, open-source and integrated with existing software. Better sampling strengthens the structure-property knowledge used to design new materials with properties suited to a given problem.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Tai-Sung Lee
Rutgers University-New Brunswick, NJ·Chemistry
Build a Python-based executing environment that connects AMBER's molecular dynamics engine, a free energy module and user workflow control, so new simulation methods can be added and prototyped quickly.
Cheng-Hsuan Lu
University at Albany, NY·Earth & Environmental Science
Integrate the MUSICA chemistry and aerosol library into the MPAS-A global weather model to create CheMPAS-A, which simulates atmospheric chemistry seamlessly from global to local scales.
Hang Liu
Rutgers University-New Brunswick, NJ·Computer Science·Early-career
Design new ways to construct and update transition probabilities for Monte Carlo graph sampling and random walk methods so sampling on GPUs runs faster.
The school
Wayne State University: 21 labs on PI Pages, $292M a year on research, 194 research doctorates a year.
Reach out
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