Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy

Qidong Lin; Yaolong Zhang; Bin Zhao; Bin Jiang

doi:10.1063/5.0004944

Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy

J Chem Phys. 2020 Apr 21;152(15):154104. doi: 10.1063/5.0004944.

Authors

Qidong Lin¹, Yaolong Zhang¹, Bin Zhao², Bin Jiang¹

Affiliations

¹ Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China.
² Theoretische Chemie, Fakultät für Chemie, Universität Bielefeld, Universitätsstraße 25, D-33615 Bielefeld, Germany.

PMID: 32321263
DOI: 10.1063/5.0004944

Abstract

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 without the 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.