ToPolyAgent: AI agents for coarse-grained bead-spring topological polymer simulations
Open accessAbstract
We introduce ToPolyAgent, a multi-agent AI framework for performing coarse-grained molecular dynamics (MD) simulations of topological polymers through natural language instructions.
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These papers typically split the workflow into role-specific agents for planning, structure retrieval, simulation execution, analysis, and quality control.
“These papers typically split the workflow into role-specific agents for planning, structure retrieval, simulation execution, analysis, and quality control [18,19,21,25,26,35-38,41,42].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [38]
Section 2.1 (page not provided) describes role-specific Config, Simulation, Workflow, and Report Agents, including simulation execution and analysis. It does not describe agents for structure retrieval or quality control; Section 4 discusses a planning agent only as a possible future addition. Thus this paper supports the role-specific workflow in part, but not the full list attributed to it. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSome studies are simulator-native: the agent configures or executes atomistic or thermodynamic tools directly.
“Some studies are simulator-native: the agent configures or executes atomistic or thermodynamic tools directly [21,25,28,29,31,33,36,38-42].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [38]
Section 2.2 (Tools for Agents) says the runLAMMPS tool executes simulations using parameters supplied by the agent, supporting the simulator-native part of the claim. Section 2.3 (Molecular Dynamics) specifies that these are coarse-grained bead-spring polymer simulations; it does not describe the tools as atomistic or specifically thermodynamic. (LLM, conf=0.82)
Experimental — accuracy not yet measuredOther papers were more demonstrative, emphasizing workflow feasibility, design navigation, or case-based success rather than tightly standardized metrics.
“Other papers were more demonstrative, emphasizing workflow feasibility, design navigation, or case-based success rather than tightly standardized metrics [17,18,21,22,35,37-39,41].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [38]
Papers such as SmartCIF, DynaMate, VASPilot, Matty, GENIUS, and ToPolyAgent show that a substantial share of the current advance lies not in replacing scientific judgment with text generation, but in making brittle infrastructural steps programmable and recoverable.
“Papers such as SmartCIF, DynaMate, VASPilot, Matty, GENIUS, and ToPolyAgent show that a substantial share of the current advance lies not in replacing scientific judgment with text generation, but in making these brittle infrastructural steps programmable and recoverable [21,25,37-39,42].”
From From Scientific Copilots to Tool-Grounded Autonomy: AI Agents in Simulation-Driven Materials Discovery (2026) · cites this paper as [38]
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- Authors
- Ding, L., Carrillo, J.-M., & Do, C.
- Affiliations
- Oak Ridge National Laboratory (US)
- Corresponding author
- Ding, Lijie
- Do, Changwoo
- Venue
- Digital Discovery
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2026
- Volume
- 5
- Issue
- 2
- Pages
- 901-909
- ISSN
- 2635-098X (ISSN-L)
- OpenAlex
- W4415270367
- Cited by
- 11
- References
- 67
- Citations by year
- 2026: 11
- FWCI
- 27.89
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Digital Discovery (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Laboratory Directed Research and Development
- Oak Ridge National Laboratory
- UT-Battelle
- Topics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- multi-agent AI, coarse-grained molecular dynamics, topological polymers, bead-spring model, natural language instructions
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