A Multi-Agent Framework for Automated Coarse-Grained Molecular Dynamics of Polymers

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.

Coarse-grained (CG) molecular dynamics enables simulations of atomic systems such as biomolecules at timescales inaccessible to all-atom (AA) methods, but existing CG neural potentials trained via force matching capture only the gradient of the free-energy surface, leaving its curvature unconstrained. We introduce a framework that augments force matching with stochastic Hessian-vector product (HVP) matching, instilling second-order curvature information into CG potentials without constructing the full Hessian. We derive a decomposition of the target CG Hessian into a model-independent projected AA Hessian, precomputed once before training, and a model-dependent covariance correction computed online at negligible cost. We construct an unbiased stochastic estimator of the Hessian-matching objective by using random probe vectors. We evaluate our method by comparing against force matching on a benchmark of nine fast-folding proteins unseen during training. HVP matching outperforms plain force matching on 8 of 9 proteins on slow-mode metrics, with reductions of up to 85% in the Kullback--Leibler divergence between the CG and reference distributions along the slowest collective mode of the largest protein. Our results demonstrate that higher-order physical supervision is a practical path to more accurate and transferable CG potentials for biomolecular simulation.

Long-running molecular simulation campaigns require repeated continuation from saved states, provenance-aware progression, adaptive assessment, and occasional interpretation of workflow conditions that cannot be resolved safely by fixed rules. Here, we present Agent-MD, a framework that places large language model (LLM) reasoning selectively at campaign construction and event-triggered review, while routine simulation, analysis, continuation, archiving, and state progression are handled by a persistent rule-based campaign agent using approved policies and explicit state records. Agent-MD was demonstrated in a grand canonical Monte Carlo-molecular dynamics (GCMC-MD) water-vapor desorption campaign comprising five montmorillonite systems and three sequential relative-humidity states (RH = 0.9-0.3-0.1). Across 15 system-RH states, the workflow completed 120 segmented simulation cycles with state-specific sampling lengths and provenance-aware restart inheritance. Routine production required no live reasoning-agent invocation, while one state reached a review boundary; two preserved incidents were subsequently evaluated through blinded reasoning-agent replay, which identified the underlying workflow problems and recommended appropriate follow-up actions. The simulations also revealed distinct composition-dependent low-RH responses, with Ca-bearing montmorillonite retaining more interlayer water and maintaining a larger basal spacing than the Na- and K-bearing systems, while the highest-charge Na system retained more residual water under dry conditions. These results demonstrate that long-running scientific workflows need not place every operation inside an LLM reasoning loop: selective reasoning can instead be combined with deterministic execution, structured evidence, and validated control handoffs to provide reproducible and auditable agent-assisted molecular simulation.