LARA: Validation-Driven Agentic Supercomputer Workflows for Atomistic Modeling

TL;DR

LARA-HPC is a validation-driven agentic framework that enhances the reliability and correctness of atomistic simulation workflows on HPC systems through validation, controlled execution, and iterative refinement.

Contribution

This work introduces LARA-HPC, a novel framework combining validation and agentic pipelines to improve robustness and correctness in HPC atomistic modeling workflows.

Findings

Validation-driven generation improves robustness of workflows.

Dry-run validation enables error detection without resource costs.

Iterative correction reduces inconsistencies in simulations.

Abstract

Large language models (LLMs) and agentic systems have recently demonstrated potential for automating scientific workflows, including atomistic simulations. However, their deployment in high-performance computing (HPC) environments remains limited by the lack of mechanisms ensuring correctness, reproducibility, and safe interaction with computational resources. Generated workflows suffer from inconsistencies, incorrect API usage, or invalid physical configurations - leading to failed or unreliable simulations. In this work, we introduce LARA-HPC, a validation-driven agentic framework to enable reliable workflow generation for atomistic modeling on HPC systems. Our approach is based on three key components: (i) a controlled execution layer that mediates all interactions with HPC resources; (ii) simulation-native validation through dry-run capabilities, enabling execution-level verification without incurring resource cost; and (iii) a multi-phase agentic pipeline combining retrieval-augmented generation and iterative refinement. We demonstrate the effectiveness of this approach performing an end-to-end atomistic simulation workflow on HPC by applying LARA-HPC to Density Functional Theory simulations. The results show that validation-driven generation significantly improves robustness and enables iterative correction of both syntactic and physical inconsistencies. More broadly, this work advocates for a shift from generation-first to validation-first paradigms in Artificial Intelligence (AI) assisted scientific computing. We argue that the future task of the computational physics community is to develop domain specific agentic systems based on structured tooling to realize an HPC enabled co-piloted research ecosystem.