Machine learning coarse-grained models of dissolutive wetting: a droplet on soluble surfaces

Abstract

Dissolutive wetting is not only a key problem in application fields such as energy, medicine, micro-devices and etc., but also a frontier issue of academic research. As an important tool for exploring the micro-mechanisms of dissolutive wetting, molecular dynamics simulations are limited by simulation scale and force field parameters. Thus, artificial intelligence is introduced into the multi-scale simulation framework to tackle such challenges. By combining density functional theory, molecular dynamics simulations and experiments, we obtain a coarse-grained model of the glucose–water dissolution pair. Furthermore, the structure of the solid molecules and the hydration shell near the solute particles are calculated by quantum mechanics/molecular mechanics to verify the accuracy of the model. Finally, the applicability of the coarse-grained model in dissolutive wetting is proven by experimental results. We believe our machine learning method not only lays a foundation for exploring the micro-mechanisms of dissolutive wetting, but also provides a general approach for obtaining the force field parameters of different systems.