Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Orchestration
High-Throughput Screening
Exascale Computing
LLM-based Agents
Materials Discovery
Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System
🤖 AI Summary
This work addresses the performance bottleneck in large-scale materials screening caused by single-agent architectures and sequential tool invocation, which fail to leverage the parallel capabilities of supercomputers. To overcome this limitation, the authors propose a hierarchical multi-agent framework tailored for supercomputing environments: a central planning agent dynamically partitions tasks, while parallel execution agents collaboratively process them. Efficient orchestration is achieved through integration with a shared Model Context Protocol server and the Parsl workflow engine. This approach establishes the first scalable multi-agent orchestration paradigm that breaks the parallelization barrier of large language models in scientific automation. Demonstrated on the Aurora supercomputer using the gpt-oss-120b model, the system successfully performs high-throughput screening of atmospheric water harvesting candidates from the CoRE MOF database, achieving high task completion rates with minimal orchestration overhead.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-agent orchestration
high-throughput screening
large language models
HPC
workflow automation
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