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One hidden yet important issue for developing neural network potentials(NNPs) is the choice of training algorithm. Here we compare the performance oftwo popular training algorithms, the adaptive moment estimation algorithm(Adam) and the Extended Kalman Filter algorithm (EKF), using theBehler-Parrinello neural network (BPNN) and two publicly accessible datasets ofliquid 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 implementingEKF in TensorFlow. It is found that NNPs trained with EKF are more transferableand less sensitive to the value of the learning rate, as compared to Adam. Inboth cases, error metrics of the validation set do not always serve as a goodindicator for the actual performance of NNPs. Instead, we show that theirperformance correlates well with a Fisher information based similarity measure.

Training Algorithm Matters for the Performance of Neural Network Potential: A Case Study of Adam and the Kalman Filter Optimizers