PolyJarvis: An LLM-Orchestrated Agent for Automated All-Atom Molecular Dynamics of Amorphous Homopolymers
About
All-atom molecular dynamics (MD) simulations can predict polymer properties from molecular structure, yet their execution requires specialized expertise in force field selection, system construction, equilibration, and property extraction. We present PolyJarvis, an agent that couples a large language model (LLM) with established simulation toolkits, including Enhanced Monte Carlo (EMC) for system construction and LAMMPS for molecular dynamics, through Model Context Protocol (MCP) servers, enabling end-to-end polymer property prediction from natural language input. Given a polymer name or SMILES string, PolyJarvis orchestrates molecular model construction, equilibration, and thermal/mechanical property calculation. Validation is conducted on nine amorphous homopolymers spanning seven chemistries: polyethylene (PE), polystyrene (PS), poly(methyl methacrylate) (PMMA), poly(ethylene glycol) (PEG), poly(ether ether ketone) (PEEK), poly(vinyl chloride) (PVC), poly(lactic acid) (PLA), polysulfone (PSU), and cis-polybutadiene (cis-PBD). On the replicate mean over four runs, 18 of the 25 property comparisons with experimental references meet the acceptance criteria (glass transition within 50K, density within 5%, bulk modulus within 30%): glass transition 7 of 9, density 5 of 9, and bulk modulus 6 of 7. The failures fall into two groups: polymer consistent force field (PCFF) systems that run under-dense, and the rigid backbones PLA and PEEK, which overestimate the glass transition on cooling. Each was traced to a protocol or an analysis step of the workflow. As a proof of concept, this work shows that an LLM-driven agent can carry out end-to-end polymer MD workflows, with predictive accuracy that varies across properties and polymers.