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.
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
3 Pith papers cite this work, alongside 430 external citations. Polarity classification is still indexing.
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aim2dat is a new Python toolkit providing interfaces for database queries, high-throughput DFT workflows, and machine learning integration to handle large material datasets.
Transition path sampling serves as an active learning engine to build machine-learned potentials accurate in barrier regions, enabling discovery of multiple protonation mechanisms in CO2 reduction on copper.
citing papers explorer
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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.
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aim2dat: A Python infrastructure for automated ab initio material modeling and data analysis
aim2dat is a new Python toolkit providing interfaces for database queries, high-throughput DFT workflows, and machine learning integration to handle large material datasets.
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Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials
Transition path sampling serves as an active learning engine to build machine-learned potentials accurate in barrier regions, enabling discovery of multiple protonation mechanisms in CO2 reduction on copper.