SimMOF: AI agent for Automated MOF Simulations

TL;DR

SimMOF is an AI-powered multi-agent framework that automates the entire process of MOF simulations from natural language queries, making complex workflows accessible and scalable.

Contribution

It introduces a novel multi-agent system that translates natural language requests into executable MOF simulation workflows, enhancing automation and decision-making.

Findings

Enables end-to-end automation of MOF simulations from natural language queries.

Represents a scalable foundation for data-driven MOF research.

Demonstrates adaptive workflows reflecting human researcher behavior.

Abstract

Metal-organic frameworks (MOFs) offer a vast design space, and as such, computational simulations play a critical role in predicting their structural and physicochemical properties. However, MOF simulations remain difficult to access because reliable analysis require expert decisions for workflow construction, parameter selection, tool interoperability, and the preparation of computational ready structures. Here, we introduce SimMOF, a large language model based multi agent framework that automates end-to-end MOF simulation workflows from natural language queries. SimMOF translates user requests into dependency aware plans, generates runnable inputs, orchestrates multiple agents to execute simulations, and summarizes results with analysis aligned to the user query. Through representative case studies, we show that SimMOF enables adaptive and cognitively autonomous workflows that reflect the iterative and decision driven behavior of human researchers and as such provides a scalable foundation for data driven MOF research.