diff --git a/docs/src/architectures/soap-bpnn.rst b/docs/src/architectures/soap-bpnn.rst index f8e1ab607..21aea43b4 100644 --- a/docs/src/architectures/soap-bpnn.rst +++ b/docs/src/architectures/soap-bpnn.rst @@ -9,7 +9,7 @@ SOAP-BPNN This is a Behler-Parrinello neural network :footcite:p:`behler_generalized_2007` with using features based on the Smooth overlab of atomic positions (SOAP) -:footcite:p:`bartok_representing_2013`. The SOAP features are calculated wit `rascaline +:footcite:p:`bartok_representing_2013`. The SOAP features are calculated with `rascaline `_. Installation diff --git a/src/metatrain/experimental/pet/trainer.py b/src/metatrain/experimental/pet/trainer.py index afe8c09dd..dbe6f1e1e 100644 --- a/src/metatrain/experimental/pet/trainer.py +++ b/src/metatrain/experimental/pet/trainer.py @@ -117,7 +117,7 @@ def train( checkpoint_path = None ######################################## - # STARTNG THE PURE PET TRAINING SCRIPT # + # STARTING THE PURE PET TRAINING SCRIPT # ######################################## logging.info("Initializing PET training...") @@ -165,6 +165,20 @@ def train( f"CUDA is deterministic: {FITTING_SCHEME.CUDA_DETERMINISTIC}" ) + st = """ +Legend: LR -> Learning Rate + MAE -> Mean Square Error + RMSE -> Root Mean Square Error + V-E-MAE/at -> MAE of the Energy per atom on the Validation set + V-E-RMSE/at -> RMSE of the Energy per atom on the Validation set + V-F-MAE -> MAE of the Forces on the Validation set + V-F-RMSE -> RMSE of the Forces on the Validation set + T-E-MAE/at -> MAE of the Energy per atom on the Training set + T-E-RMSE/at -> RMSE of the Energy per atom on the Training set + T-F-MAE -> MAE of the Forces on the Training set + T-F-RMSE -> RMSE of the Forces on the Training set +Units of the Energy and Forces are the same units given in input""" + training_configuration_log += st logging.info(training_configuration_log) set_reproducibility(