MLRNet: Combining physics-motivated potential model with neural network for intermolecular potential energy surface construction

A physics-based machine learning model called MLRNet has been developed to construct the high-accuracy two-body intermolecular potential energy surface (IPES). The outputs of the neural network are integrated into the physically realistic Morse/Long Range (MLR) function, which ensures that the MLRNet has meaningful extrapolation at both short and long ranges and solves the asymptotic problem in common neural network potential (NNP) models. The neural network representation of the MLR parameters is more flexible and more efficient than the polynomials expansion in the conventional mdMLR model, especially for systems containing non-rigid monomer(s). The present work illustrates the basic framework of the current MLRNet model, including (i) how to combine the physically meaningful MLR function with different possible NN structures, (ii) the preservation of permutation symmetry, and (iii) the predetermination of the long-range function uLR. We choose two realistic systems to demonstrate the performance of MLRNet: the 3-dimensional IPES of CO2-He including CO2 anti-symmetric vibration Q3, and the 6-dimensional IPES of H2O-Ar system. In both cases, the fitting errors of the MLRNet are several times smaller than those of the conventional mdMLR model. Both the short-range and long-range extrapolation tests have been performed to illustrate the extrapolation ability of the MLRNet and its damping function version. Moreover, for the 6-D H2O-Ar system, the MLRNet only needs 1596 trainable parameters which are almost equal 5-D mdMLR model (1509) and half of the PIP-NN model (3501) with similar accuracy, which illustrates the model efficiency in high-dimensional IPES fitting.