Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations

We present a novel, flexible framework for electronic structure interfaces designed for nonadiabatic dynamics simulations, implemented in Python 3 using concepts of object-oriented programming. This framework streamlines the development of new interfaces by providing a reusable and extendable code base. It supports the computation of energies, gradients, various couplingslike spin–orbit couplings, nonadiabatic couplings, and transition dipole momentsand other properties for an arbitrary number of states with any multiplicities and charges. A key innovation within this framework is the introduction of hybrid interfaces, which can use other interfaces in a general hierarchical manner. Hybrid interfaces are capable of using one or more child interfaces to implement multiscale approaches, such as quantum mechanics/molecular mechanics where different child interfaces are assigned to different regions of a system. The concept of hybrid interfaces can be extended through nesting, where hybrid parent interfaces use hybrid child interfaces to easily setup complex workflows without the need for additional coding. We demonstrate the versatility of hybrid interfaces with two examples: one at the method level and one at the workflow level. The first example showcases the numerical differentiation of wave function overlaps, implemented as a hybrid interface and used to optimize a minimum-energy conical intersection with numerical nonadiabatic couplings. The second example presents an adaptive learning workflow, where nested hybrid interfaces are used to iteratively refine a machine learning model. This work lays the groundwork for more modular, flexible, and scalable software design in excited-state dynamics.