ML Interfaces

ML interfaces provide training and inference hooks for machine-learned potentials.

Available ML interface modules

ALF currently includes two ML interface modules:

1. alframework.ml_interfaces.hippynn_interface HIPPYNN-based training and model-management utilities, including support for ensemble workflows used during active learning.

2. alframework.ml_interfaces.neurochem_interface NeuroChem/ANI training and calculator interface utilities for ANI-based models and ensembles.

Choosing an ML interface

Use hippynn_interface when your workflow is centered on HIPPYNN models and you want ALF to orchestrate retraining in the active-learning loop.

Use neurochem_interface when your workflow depends on ANI/NeuroChem tooling and model formats.

In both cases, the selected ML task is configured through your ALF master and ML configuration JSON files.

ML interface API links

You can link from this guide directly to API pages:

• ML interfaces package API

• HIPPYNN interface module

• NeuroChem interface module

Related examples

See Simple Water Example, Molten Salt Example, and Reactive Sampling Example for HIPPYNN-based active-learning workflows.