Deep Learning of Lennard-Jones Potential Parameterization

In this study, a deep neural network is developed to parameterize van der Waals interactions at various thermodynamic states based on the pair-correlation functions obtained through MD simulation of about 52 μs. After training, the network not only performs with high accuracy and fidelity for van der Waals particles but also performs well for correlations obtained from all-atom MD simulation of complex molecules. The network is capable of developing coarse-grained force fields within the theoretical limitation and accuracy imposed by van der Waals interactions. The accuracy and fidelity of the method are investigated by computing the total variation in the radial distribution function and the Kullback-Leibler divergence for the coarse-grained model development, while the mean-squared error is used to characterize the performance for the vdW particles. Our results show that deep learning is able to obtain the solution to inverse-problem of liquid-state theory under the assumption of a predetermined pair potential in both all-atom and coarse-grained models with a computational cost that is several orders of magnitude faster than other available methods in the literature.