ToPolyAgent: AI Agents for Coarse-Grained Topological Polymer Simulations

TL;DR

ToPolyAgent tackles the barrier of performing coarse-grained MD simulations of topological polymers by introducing a four-agent, LLM-enabled framework that translates natural-language prompts into configuration, simulation, and reporting tasks. The two operational modes—interactive and autonomous—enable both user-guided refinement and end-to-end execution across various architectures, including linear, ring, brush, star, and dendrimers. The work demonstrates MD workflows with LJ and FENE potentials, thermostat schemes, and a suite of conformational metrics such as $R_g$, $R_{ee}$, $l_p$, $P(q)$, and $g(r)$. The approach lowers entry barriers, supports AI-assisted materials discovery, and lays groundwork for extensible AI-driven scientific workflows in polymer science.

Abstract

We introduce ToPolyAgent, a multi-agent AI framework for performing coarse-grained molecular dynamics (MD) simulations of topological polymers through natural language instructions. By integrating large language models (LLMs) with domain-specific computational tools, ToPolyAgent supports both interactive and autonomous simulation workflows across diverse polymer architectures, including linear, ring, brush, and star polymers, as well as dendrimers. The system consists of four LLM-powered agents: a Config Agent for generating initial polymer-solvent configurations, a Simulation Agent for executing LAMMPS-based MD simulations and conformational analyses, a Report Agent for compiling markdown reports, and a Workflow Agent for streamlined autonomous operations. Interactive mode incorporates user feedback loops for iterative refinements, while autonomous mode enables end-to-end task execution from detailed prompts. We demonstrate ToPolyAgent's versatility through case studies involving diverse polymer architectures under varying solvent condition, thermostats, and simulation lengths. Furthermore, we highlight its potential as a research assistant by directing it to investigate the effect of interaction parameters on the linear polymer conformation, and the influence of grafting density on the persistence length of the brush polymer. By coupling natural language interfaces with rigorous simulation tools, ToPolyAgent lowers barriers to complex computational workflows and advances AI-driven materials discovery in polymer science. It lays the foundation for autonomous and extensible multi-agent scientific research ecosystems.

Figures (8)

• Figure 1: Overview of the ToPolyAgent workflow. The system operates in two modes: interactive and autonomous. Four sets of tools are assigned to four distinct agents. These agents execute molecular dynamics simulations of various topological polymers under different conditions based on user instructions.
• Figure 2: Example of ToPolyAgent assisting in a brush polymer simulation under interactive mode. Detailed text outputs from the agents are omitted for brevity. (a) The Config Agent and the user discuss the system configuration. (b) The Simulation Agent performs the MD simulation and presents the final system configuration and analysis results. (c) The user requests the Simulation Agent to extend the simulation, and the Report Agent compiles and presents a comprehensive report
• Figure 3: Conformation analysis of a brush polymer generated by ToPolyAgent. The system parameters are $N_b = 20$, $\sigma_g = 0.6$, $N_s = 5$, $n_s = 0.3$, $\epsilon_{pp} = \epsilon_{ss} = 0.3$, and $\epsilon_{sp} = 1.5$, with a simulation length of 100,000 steps under a Langevin thermostat. (a) Radius of gyration analysis. (b) Mean square displacement (MSD) analysis for diffusivity $D$ fitting. (c) Scattering function, or form factor $P(q)$. (d) Pair distribution function $g(r)$ with a snapshot of the polymer configuration inset. (e) Persistence length analysis. (f) End-to-end distance analysis. MSD, $P(q)$, and $g(r)$, as well as the mean values of $R_g^2$, $l_p$, and $R_{ee}$, are calculated using the second half of the simulation data.
• Figure 4: Example of ToPolyAgent assisting in a star polymer simulation under interactive mode.
• Figure 5: Examples of ToPolyAgent operating in autonomous mode. (a) Simulation of a linear polymer using the Nosé–Hoover thermostat. (b) Simulation of a ring polymer with specified interaction parameters. (c) Simulation of a dendrimer with a specified simulation box size.