PolyJarvis: An LLM-Orchestrated Agent for Automated All-Atom Molecular Dynamics of Amorphous Homopolymers
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Abstract (arXiv)
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, a platform in which a planning agent produces a validated run plan that deterministic stage scripts execute through established simulation toolkits, Enhanced Monte Carlo (EMC) for system construction and LAMMPS for molecular dynamics, exposed as Model Context Protocol (MCP) servers, with a recovery agent consulted only on structured failures and within a fixed decision budget. Given a repeat-unit SMILES string and target properties, PolyJarvis constructs the amorphous cell, equilibrates it under a mechanized convergence gate, and computes target properties. Validation is conducted on seven amorphous homopolymers, each run as three replicates that share a protocol frozen per system and use independent random seeds, namely polyethylene (PE), atactic polystyrene (aPS), syndiotactic poly(vinyl chloride) (sPVC), poly(L-lactic acid) (PLLA), poly(ethylene glycol) (PEG), poly(ether ether ketone) (PEEK), and polysulfone (PSU). Against experimental references, 13 of 19 graded comparisons meet the acceptance criteria (density 5 of 7, glass transition 4 of 7, bulk modulus 4 of 5). The failures are concentrated in the PCFF systems: under-density of aPS and PEG, overestimated glass transitions of the stiff PLLA and PEEK backbones, and an overstiff PEG bulk modulus.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 8 of its 64 references that are in the map. First posted on arXiv 2 Apr 2026.
Builds on
8 of 64 references.
References · 8
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- 2024AlloyBERT: Alloy Property Prediction with Large Language Modelscited by 5
- 2026Polymer-Agent: Large Language Model Agent for Polymer Designcited by 3
- 2026From Natural Language to Materials Discovery:The Materials Knowledge Navigation Agentcited by 1
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Citing papers per year: 2026: 6.
6 papers.
- 2026MDGYM: Benchmarking AI Agents on Molecular Simulationscited by 3
- 2026Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflowscited by 2
- 2026MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedbackcited by 2
- 2026MDArena: Evaluating Coding Agents on Realistic Molecular Dynamics Workflowscited by 2
- 2026Material Database Agent: A Multimodal Agentic Framework for Scientific Literature Miningcited by 0
- 2026Science Done on a Machine by a Machine: AI Agents in Computational Chemistrycited by 0
- 2026Science Done on a Machine by a Machine: AI Agents in Computational Chemistry19 Aug 2026
- 2026MDArena: Evaluating Coding Agents on Realistic Molecular Dynamics Workflows31 Jul 2026
- 2026MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback11 Jun 2026
- 2026Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows14 May 2026
- 2026MDGYM: Benchmarking AI Agents on Molecular Simulations9 May 2026
- 2026Material Database Agent: A Multimodal Agentic Framework for Scientific Literature Mining5 May 2026
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