Multiscale Workflows
Multiscale Simulation And Reliable Computational Workflows
Research question: How can complex molecular simulations be made reproducible, comparable, and practical for systematic studies rather than one-off calculations?
Overview
Complex simulations are useful only when their assumptions, inputs, sampling, and analysis are traceable. Kabir Lab develops and documents workflows that connect electronic structure with molecular dynamics, explicit solvent, QM/MM modeling, conformational sampling, and free-energy analysis. The goal is reproducible scientific comparison, not automation for its own sake. That means method choices, structural preparation, charge models, sampling decisions, and post-processing steps must be visible enough for students and collaborators to inspect and repeat.
This research direction supports studies of flavoprotein photophysics, chromophore spectral tuning, explicit-solvent quantum chemistry setup, and association free energies. It also creates a training path for undergraduate researchers: students can learn Linux, Python, molecular visualization, simulation setup, version control, and evidence-focused documentation while contributing to scientific workflows.
Why It Matters
Computational chemistry can become fragile when scripts, parameters, or system-preparation decisions are hidden. Reproducible workflows make it easier to compare systems, teach new researchers, revisit assumptions, and identify where uncertainty enters a mechanistic conclusion.
How We Study It
Kabir Lab connects electronic structure, molecular dynamics, QM/MM, conformational sampling, explicit-solvent setup, and analysis through documented Python and shell workflows. The goal is reproducible scientific comparison, not automation as a substitute for scientific judgment.
Public Resources And Examples
Notes On Maturity
Some public workflows depend on legacy research environments such as Python 2, cluster-specific scripts, or specialized chemistry packages. They are presented as research workflows or legacy protocols unless the PI later confirms active maintenance status.
Student Involvement
Students can contribute by testing setup instructions, documenting assumptions, improving analysis notebooks, comparing outputs across molecular systems, and turning one-off calculations into reproducible research records.