Multi-agentic AI framework for end-to-end atomistic simulations

Abstract

One of the main bottlenecks for the wide adoption of atomistic simulation pipelines for computational materials design is the high complexity of the workflows which many times requires the use of a diverse set of specialized toolkits and libraries. Here, we introduce a multi-agent artificial intelligence (AI) framework that autonomously performs end-to-end atomistic simulations, i.e. molecular dynamics (MD), with automated input and associated full suite of analyses, using large language models (LLMs) and multiple specialized AI agents. Our system orchestrates the entire simulation pipeline, from structure generation via Atomsk and interatomic potential discovery through automated web mining, to simulation setup and execution using LAMMPS on high-performance computing (HPC) platforms. Post-simulation, our agentic framework performs automated data analysis and visualization with popular analysis tools like OVITO and Phonopy. Each expert agent operates within a defined role, equipped with domain-specific functions and a shared memory context for coordination. Using a diverse set of representative elemental and alloy systems, we demonstrate the capability of our framework to execute a range of static and dynamic materials modeling tasks, including lattice parameter and cohesive energy estimation, elastic constants computation, phonon dispersion analysis, as well as perform MD simulations to determine dynamical properties that aid estimation of melting point. The results produced by the agents show strong agreement with those obtained by a human expert, highlighting the reliability of the agentic approach. By combining automation, reproducibility, and human-in-the-loop control, our framework lowers the barrier to the widespread adoption of scalable, AI-driven discovery tools in materials science.