cs.AIApr 9, 2026

Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System

Authors: Thang Duc Pham, Harikrishna Tummalapalli, Fakhrul Hasan Bhuiyan, Álvaro Vázquez Mayagoitia, Christine Simpson, Riccardo Balin, Venkatram Vishwanath, Murat Keçeli

Organizations: Computational Science (CPS) Division, Argonne National Laboratory, Lemont IL 60439, USA · Argonne Leadership Computing Facility (ALCF), Argonne National Laboratory, Lemont IL 60439, USA

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Abstract

The integration of Artificial Intelligence (AI) with High-Performance Computing (HPC) is transforming scientific workflows from human-directed pipelines into adaptive systems capable of autonomous decision-making. Large language models (LLMs) play a critical role in autonomous workflows; however, deploying LLM-based agents at scale remains a significant challenge. Single-agent architectures and sequential tool calls often become serialization bottlenecks when executing large-scale simulation campaigns, failing to utilize the massive parallelism of exascale resources. To address this, we present a scalable, hierarchical multi-agent framework for orchestrating high-throughput screening campaigns. Our planner-executor architecture employs a central planning agent to dynamically partition workloads and assign subtasks to a swarm of parallel executor agents. All executor agents interface with a shared Model Context Protocol (MCP) server that orchestrates tasks via the Parsl workflow engine. To demonstrate this framework, we employed the open-weight gpt-oss-120b model to orchestrate a high-throughput screening of the Computation-Ready Experimental (CoRE) Metal-Organic Framework (MOF) database for atmospheric water harvesting. The results demonstrate that the proposed agentic framework enables efficient and scalable execution on the Aurora supercomputer, with low orchestration overhead and high task completion rates. This work establishes a flexible paradigm for LLM-driven scientific automation on HPC systems, with broad applicability to materials discovery and beyond.

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Figure 1: Schematic of the scalable multi-agent orchestration architecture, with a central planner agent, dynamically allocated number of executor agents, a data analyst agent and MCP servers.
Figure 2: Representative outputs from an agentic workflow used for high-throughput screening of 1,152 MOFs for atmospheric water harvesting. The figure illustrates the end-to-end interaction, including the human query, the planner agent’s task decomposition, the executor agent’s tool invocation and corresponding outputs, and the data analyst agent’s post-processing steps and final response. For clarity, both the user query and agent outputs are abbreviated using […].
Figure 3: Distribution of working capacities of water for the screened 2,304 Metal-Organic Frameworks (MOFs), calculated between 1920 Pa (adsorption) and 320 Pa (desorption) at 298 K, from the 256 nodes weak-scaling run. The violin plot illustrates the probability density of the dataset, while the overlaid strip plot represents individual MOF candidates. The red-dashed line marks the 80th percentile (top 20% cutoff).
Figure 4: Scaling performance of the multi-agent orchestration workflow. (a) Weak scaling with a constant workload of 9 MOFs per node across 1 to 256 nodes. (b) Strong scaling with a fixed workload of 5,591 MOFs (11,182 simulations) across 8 to 256 compute nodes.

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