ChemGraph as an agentic framework for computational chemistry workflows

Atomistic simulations are essential in chemistry and materials science but remain challenging to run due to the expert knowledge required for the setup, execution, and validation stages of these calculations. We present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. We evaluate ChemGraph across…

• UD
  U.S. Department of Energy

  Awards: AC02-06CH11357, DE-AC02, 06CH11357, DE-AC02-06CH11357, DE-AC02-
• OO
  Office of Science

  Awards: DE-AC02-06CH11357, DE-AC02, 06CH11357, AC02-06CH11357
• AS
  Advanced Scientific Computing Research
• AN
  Argonne National Laboratory

  Awards: DE-AC02, 06CH11357, AC02-06CH11357