  We present an open-source MLatom@XACS software ecosystem for on-the-fly
surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev
(LZBL) algorithm. The dynamics can be performed via Python API with a wide
range of quantum mechanical (QM) and machine learning (ML) methods, including
ab initio QM (CASSCF and ADC(2)), semi-empirical QM methods (e.g., AM1, PM3,
OMx, and ODMx), and many types of machine learning potentials (e.g., KREG, ANI,
and MACE). Combinations of QM and ML methods can also be used. While the user
can build their own combinations, we provide AIQM1, which is based on
{\Delta}-learning and can be used out of the box. We showcase how AIQM1
reproduces the isomerization quantum yield of trans-azobenzene at a low cost.
We provide example scripts that, in a dozen lines, enable the user to obtain
the final population plots by simply providing the initial geometry of a
molecule. Thus, those scripts perform geometry optimization, normal mode
calculations, initial condition sampling, parallel trajectories propagation,
population analysis, and final result plotting. Given the capabilities of
MLatom to be used for training different ML models, this ecosystem can be
seamlessly integrated into the protocols building ML models for nonadiabatic
dynamics. In the future, a deeper and more efficient integration of MLatom with
Newton-X will enable vast range of functionalities for surface hopping
dynamics, such as fewest-switches surface hopping, to facilitate similar
workflows via the Python API.
