Backend Guides

PyAR can use different computational backends for different chemistry tasks. A backend may be a semiempirical program, a quantum-chemistry program, a machine-learning potential, or a hybrid route.

For most users, the practical question is simple: choose a backend that is installed, appropriate for your chemistry, and supported by the PyAR task you want to run.

Current backend families

Backend	Family	Typical role in PyAR
xtb	semiempirical	Fast optimisation, aggregation, solvation, and AFIR/geomeTRIC reaction searches.
aimnet_2	machine-learning potential	Fast molecular energy/force evaluation for optimisation and AFIR/geomeTRIC reaction searches.
orca	quantum chemistry	Higher-level DFT-style backend. The geomeTRIC route is wired, but local executable setup and validation are required.
gaussian	quantum chemistry	Higher-level DFT-style backend. The geomeTRIC route is wired, but local executable setup and validation are required.
mopac	semiempirical	Legacy semiempirical optimisation route.
obabel	molecular mechanics / OpenBabel route	Lightweight structure preparation or optimisation where charge and multiplicity handling are not central.
psi4	quantum chemistry	Available as a backend family, but not currently on the registered AFIR/geomeTRIC energy-gradient route.
turbomole / xtb_turbo	quantum chemistry / compatibility route	Legacy or compatibility workflows. New AFIR/geomeTRIC development should not depend on Turbomole coordinate updates.
xtb-aimnet2 / xtb-aiqm1	hybrid	Staged routes that combine fast pre-optimisation with ML or AIQM-style refinement where available.

Which backend should I start with?

For a new user:

• Start with xtb when you want a robust and fast first calculation.

• Try aimnet_2 when you want a machine-learning-potential route and your molecule is within the chemistry where the model is expected to behave well.

• Use orca or gaussian only when the executable is installed and you have validated a small test case on your machine.

• Use legacy or hybrid routes only when you know why you need them.

AFIR/geomeTRIC support

The current registered energy-gradient providers for geomeTRIC-backed AFIR reaction optimisation are:

xtb
aimnet_2
orca
gaussian

This means PyAR can, in principle, build the objective backend energy + AFIR bias and give it to geomeTRIC/TRIC. In practice, backend installation, executable paths, charge/multiplicity support, and local validation still matter.

Backend pages

• xTB Guide
• AIMNet2 Backend
• ORCA Backend
• Gaussian Backend
• MOPAC Backend
• OpenBabel Backend
• Psi4 Backend