ColPackAgent integrates a custom colpack Python package wrapping HOOMD-blue with MCP tools and an agent skill to enable reliable autonomous workflows for colloidal packing simulations across interactive, prompt-driven, and autoresearch modes.
Ramasubramani , author B
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
Systematic benchmarking finds Grønbech-Jensen-Farago Langevin thermostat most consistent for temperature and energy sampling in binary LJ glass simulations, at roughly double the cost and with friction-dependent diffusion.
citing papers explorer
-
ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packing
ColPackAgent integrates a custom colpack Python package wrapping HOOMD-blue with MCP tools and an agent skill to enable reliable autonomous workflows for colloidal packing simulations across interactive, prompt-driven, and autoresearch modes.
-
Benchmarking thermostat algorithms in molecular dynamics simulations of a binary Lennard-Jones glass-former model
Systematic benchmarking finds Grønbech-Jensen-Farago Langevin thermostat most consistent for temperature and energy sampling in binary LJ glass simulations, at roughly double the cost and with friction-dependent diffusion.