aiida-learn

Generalized active-learning workflows for the automated training of machine-learning interatomic potentials, interfacing the NequIP, Allegro, and FLARE codes with first-principles calculations.

The package implements uncertainty-quantification and deduplication strategies to select, on-the-fly, only the configurations that are actually informative, making first-principles thermodynamics and phase transitions affordable.

Currently a private repository.