El Agente Potente: High-Throughput Agentic Atomistic Simulations

2026-09-13

AI Summary

研究解决了高通量原子模拟中保持工作流严谨性的问题，通过结合类型执行图和编码模式，利用机器学习势函数驱动的El Agente Potente系统实现。

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

Problem

• machine-learning interatomic potentials
• high-throughput property calculations
• workflow rigour
• atomistic simulations

Innovation

• typed execution graphs
• machine-learning interatomic potentials (MLIPs)
• agentic system
• high-throughput simulations
• coding agent