  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 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
multifunctional model, enhancing its efficiency and effectiveness. Utilizing
Wannier functions as the basis lays the groundwork for predicting more physical
quantities. This approach serves as a powerful tool to explore both the
structural and electronic properties of large-scale systems characterized by
low periodicities. By endowing an existing 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.
