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arXiv · 2609.26657

Agent-E2MD: Autonomous Translation of Interatomic Potential Equations into Physically Validated Pair Styles for Molecular Dynamics in LAMMPS

Bilvin Varughese·Orcun Yildiz·Aditya Koneru·Henry Chan·Tom Peterka·Subramanian Sankaranarayanan
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Abstract

Interatomic potentials underpin MD and govern predictive atomistic-model fidelity for metals, semiconductors, oxides, liquids, and reactive systems. A potential has limited practical value until reliably implemented in production MD code. Slow, expertise-intensive implementation requires more than equation-to-C++ translation: it must select the neighbor-list architecture, evaluate and distribute many-body derivatives, manage interprocessor communication, and preserve host-code energy, force, and virial conventions. We introduce Agent-E2MD, a knowledge-guided agentic workflow that translates user-specified interatomic models into executable LAMMPS pair styles. It combines architectural classification, code generation, autonomous build-test-fix cycles, simulation execution, and hierarchical physical validation. We test Agent-E2MD on seven material-potential pairs: Lennard--Jones Ar, EAM/FS Ag, MEAM Bi, Tersoff Si, GAP Ni, ReaxFF C/H/N/O, and a recently developed Symbolic Regression EAM (Symb EAM) model for Al, currently unavailable in LAMMPS. Single-point results match reference energies and forces for all seven models. Five crystalline many-body benchmarks recover reference relaxed lattice properties, vacancy formation energies, and elastic constants. Ag, Bi, Si, and Ni are stable at finite temperature in nanosecond-scale simulations; ReaxFF extends validation to reactive dynamics with evolving bond order and charge equilibration. Results demonstrate that the software architecture for an interatomic potential can be inferred from its physical and mathematical structure. Agent-E2MD does not replace scientific judgment; it shifts users' effort from routine implementation to model definition, rigorous validation, and physical interpretation. The framework provides a practical, traceable bridge between emerging AI-driven potential discovery methods and production-scale atomistic simulations.

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Bilvin Varughese, Orcun Yildiz, Aditya Koneru, Henry Chan, Tom Peterka, Subramanian Sankaranarayanan. 2026-09-22. Agent-E2MD: Autonomous Translation of Interatomic Potential Equations into Physically Validated Pair Styles for Molecular Dynamics in LAMMPS. https://arxiv.org/abs/2609.26657

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