  One hidden yet important issue for developing neural network potentials
(NNPs) is the choice of training algorithm. Here we compare the performance of
two popular training algorithms, the adaptive moment estimation algorithm
(Adam) and the Extended Kalman Filter algorithm (EKF), using the
Behler-Parrinello neural network (BPNN) and two publicly accessible datasets of
liquid water [Proc. Natl. Acad. Sci. U.S.A. 2016, 113, 8368-8373 and Proc.
Natl. Acad. Sci. U.S.A. 2019, 116, 1110-1115]. This is achieved by implementing
EKF in TensorFlow. It is found that NNPs trained with EKF are more transferable
and less sensitive to the value of the learning rate, as compared to Adam. In
both cases, error metrics of the validation set do not always serve as a good
indicator for the actual performance of NNPs. Instead, we show that their
performance correlates well with a Fisher information based similarity measure.
