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.