AiiDA-TrainsPot introduces an automated workflow for training neural-network interatomic potentials via calibrated active learning on carbon allotropes and alloy phase transitions.
Behler, Representing potential energy surfaces by high-dimensional neural network potentials, Journal of Physics: Condensed Matter26, 183001 (2014)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.comp-ph 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials
AiiDA-TrainsPot introduces an automated workflow for training neural-network interatomic potentials via calibrated active learning on carbon allotropes and alloy phase transitions.