DynaMate2: Democratization of Agentic AI for Expert-Designed Custom Workflows
Orlando A. Mendible-Barreto, Ajay Vallabh, Ubaldo M. C\'ordova-Figueroa, and Yamil J. Col\'on

TL;DR
DynaMate2 is an open-source framework that simplifies integrating expert scientific tools into AI-driven workflows, enabling broader adoption in computational chemistry and materials science.
Contribution
It introduces a hierarchical agentic framework that allows researchers to convert existing Python functions into AI-callable tools without requiring code generation by LLMs.
Findings
Successfully demonstrated on a molecular dynamics workflow
Provides a step-by-step Tool Registration Protocol
Open-source with a web interface for community use
Abstract
Scientific workflows in computational chemistry and materials science typically involve multiple interdependent steps, such as model preparation, system construction, simulation execution, and data analysis, that researchers have refined over the years into highly specialized, validated codebases. While large language model (LLM) agent frameworks have demonstrated the potential to automate such workflows, existing systems are built for specific, pre-defined task sequences. Adapting them to new domains or integrating custom expert-developed tools requires substantial programming expertise, which limits their adoption across the broader scientific community. Here we present DynaMate2, a hierarchical agentic framework and open-source template whose central design goal is to lower the barrier for any researcher to convert their existing expert-defined Python functions into AI-callable tools…
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