Generalized Global Neural Network with Pairwise Charge Transfer for Fast Prediction of Dynamic Properties under an Electric Field.
Machine learning potentials (MLPs) have emerged as game-changing tools for large-scale atomic simulations, overcoming the poor-scaling limitation intrinsic to traditional quantum mechanics (QM) methods. However, accurately incorporating electronic information remains a significant challenge for MLPs, particularly in efficiently computing the dynamic properties of matter under electric fields─a task central to topics such as infrared spectroscopy, interfaces under electric fields, and ferroelectric polarization. Herein, we report a physics-informed pairwise charge-transfer (PQT) theory to derive dynamic equations for macroscopic polarization that inherently conserve fundamental physical laws. Using the PQT theory, a Generalized Global Neural Network (GGNN) enhanced with the PQT mechanism is developed for the rapid prediction of dynamic properties under electric fields, applicable to both molecules and materials across the periodic table. Specifically, a generalized global data set comprising 3.18 million structures with atomic charges for 81 elements is utilized to pretrain a GGNN patched with PQT modules. Leveraging this pretrained GGNN-PQT potential, we can conveniently sample the potential energy surface under electric fields and fine-tune the potential using a small QM data set containing exact response properties at low cost. Our GGNN-PQT has linear scaling and introduces a low computational overhead compared to the standard GGNN, yet achieves both high speeds and low scaling. We demonstrate the performance of GGNN-PQT in computing dynamic response properties across a wide range of systems, including isolated molecules, adsorbed molecules, molecular crystals, liquid water, and ferroelectric materials.