MoSDeF-GOMC: Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC

Area of Science:

• Computational Chemistry
• Materials Science
• Chemical Engineering

Background:

• Molecular simulations require complex setup procedures.
• Existing workflows can be challenging for new users.
• Reproducibility is crucial for scientific validation.

Purpose of the Study:

• To introduce MoSDeF-GOMC, a Python interface for GOMC Monte Carlo simulations.
• To automate and streamline the molecular simulation setup process.
• To enhance the accessibility and reproducibility of complex simulations.

Main Methods:

• Developed a Python interface connecting GOMC to the MoSDeF ecosystem.
• Automated generation of initial coordinates and force field parameter assignment.
• Integrated simulation setup, execution, and data analysis into single scripts.

Main Results:

• Successfully predicted CO2 adsorption in IRMOF-1.
• Calculated hydration free energies for Ne and Rn across temperatures.
• Determined the vapor-liquid coexistence curve for a jet fuel surrogate.

Conclusions:

• MoSDeF-GOMC lowers entry barriers for molecular simulations.
• The software enables complex, reproducible simulation workflows.
• It facilitates diverse applications in materials and chemical engineering.