Bridging deep learning force fields and electronic structures with a physics-informed approach

This work presents a physics-informed neural network approach bridging deep-learning force field and electronic structure simulations, illustrated through twisted two-dimensional large-scale material systems. The deep potential molecular dynamics model is adopted as the backbone, and the electronic structure simulation is integrated. Using Wannier functions as the basis, we categorize Wannier Hamiltonian elements based on physical principles to incorporate diverse information from a deep-learning force field model. This information-sharing mechanism streamlines the architecture of our dual-functional model, enhancing its efficiency and effectiveness. This Wannier-based dual-functional model for simulating electronic band and structural relaxation (WANDER) serves as a powerful tool to explore large-scale systems. By endowing a well-developed machine-learning force field with electronic structure simulation capabilities, the study marks a significant advancement in developing multimodal machine-learning-based computational methods that can achieve multiple functionalities traditionally exclusive to first-principles calculations. Moreover, utilizing Wannier functions as the basis lays the groundwork for predicting more physical quantities.