Automate molecular simulations with the ChemGraph agentic framework
ChemGraph is an agentic framework from Argonne National Lab that automates molecular simulation workflows using LLMs. Built on LangGraph and ASE (Atomic Simulation Environment), it enables natural language control of computational chemistry tasks — structure generation, geometry optimization, thermochemistry, and more. Supports DFT (NWChem, ORCA), semi-empirical (xTB), and ML potentials (MACE).
pip install chemgraph
# Or via Docker
docker pull ghcr.io/argonne-lcf/chemgraph:latest
from chemgraph import ChemGraphAgent
agent = ChemGraphAgent(
llm_provider="anthropic",
calculator="xtb", # fast semi-empirical
)
# Natural language molecular tasks
result = agent.run("Optimize the geometry of caffeine and calculate its vibrational frequencies")
print(result.energy)
print(result.frequencies)
# Thermochemistry
result = agent.run("Calculate the enthalpy of formation of ethanol at 298K")
print(f"ΔHf = {result.enthalpy:.2f} kJ/mol")
| Calculator | Type | Speed | Accuracy |
|---|---|---|---|
| xTB (TBLite) | Semi-empirical | Fast | Moderate |
| MACE | ML potential | Fast | Good |
| NWChem | Ab initio DFT | Slow | High |
| ORCA | Ab initio/DFT | Slow | High |
| UMA | Universal ML | Fast | Good |
# Multi-step workflow
workflow = agent.create_workflow([
"Generate 3D structure of aspirin from SMILES",
"Optimize geometry with DFT/B3LYP/6-31G*",
"Calculate IR spectrum",
"Identify key functional group vibrations",
])
results = workflow.execute()
# Reaction pathway
pathway = agent.run(
"Find the transition state for the Diels-Alder reaction "
"between butadiene and ethylene"
)
from ase.io import read
from chemgraph.calculators import get_calculator
# Use ChemGraph's calculator with ASE directly
atoms = read("molecule.xyz")
calc = get_calculator("xtb")
atoms.calc = calc
energy = atoms.get_potential_energy()
forces = atoms.get_forces()
ChemGraph uses LangGraph's state machine to orchestrate: