El Agente Potente: High-Throughput Agentic Atomistic Simulations

Abstract:Foundational machine-learning interatomic potentials (MLIPs) are transforming atomistic simulations by achieving near-ab initio accuracy across large chemical spaces at a fraction of the computational cost. A central challenge in using these tools for high-throughput property calculations is translating high-level scientific intent into adaptive simulation campaigns without compromising workflow rigour. We introduce El Agente Potente, an agentic system that combines typed execution graphs with a complementary coding mode for MLIPs-driven atomistic simulations. Typed execution graphs provide structured and provenance-aware execution for standardized workflows, with large language models (LLMs) restricted to planning and routing while deterministic Python components perform scientific computation and validation. Complementing this structured execution, a coding agent constructs customized workflows for tasks requiring greater procedural flexibility while invoking existing Potente functions for supported calculations. We demonstrate El Agente Potente across computational materials discovery, molecular energy-landscape exploration, adsorption, and catalytic reaction workflows, together with systematic benchmarks of reproducibility and LLM token cost. These results establish typed execution graphs and code-based workflow construction as complementary mechanisms for agentic scientific computing, combining controlled, auditable execution with the flexibility required for customized atomistic simulations

64 pages, 16 figures, and 3 tables, including Supporting Information. Main text: 24 pages, 6 figures, and 1 table

Artificial Intelligence (cs.AI); Chemical Physics (physics.chem-ph)

arXiv:2609.14840 [cs.AI]

(or arXiv:2609.14840v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.14840 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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