  An efficient and trajectory-free active learning method is proposed to
automatically sample data points for constructing globally accurate reactive
potential energy surfaces (PESs) using neural networks (NNs). Although NNs do
not provide the predictive variance as the Gaussian process regression does, we
can alternatively minimize the negative of the squared difference surface
(NSDS) given by two different NN models to actively locate the point where the
PES is least confident. A batch of points in the minima of this NSDS can be
iteratively added into the training set to improve the PES. The configuration
space is gradually and globally covered with no need to run classical
trajectory (or equivalently molecular dynamics) simulations. Through refitting
the available analytical PESs of H3 and OH3 reactive systems, we demonstrate
the efficiency and robustness of this new strategy, which enables fast
convergence of the reactive PESs with respect to the number of points in terms
of quantum scattering probabilities.
