| Autor: |
Zhang L; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China., Pios SV; Zhejiang Laboratory, Hangzhou, Zhejiang 311100, People's Republic of China., Martyka M; Faculty of Chemistry, University of Warsaw, Pasteura 1, Warsaw 02-093, Poland., Ge F; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China., Hou YF; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China., Chen Y; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China., Chen L; Zhejiang Laboratory, Hangzhou, Zhejiang 311100, People's Republic of China., Jankowska J; Faculty of Chemistry, University of Warsaw, Pasteura 1, Warsaw 02-093, Poland., Barbatti M; Aix Marseille University, CNRS, ICR, Marseille 13397, France.; Institut Universitaire de France, Paris 75231, France., Dral PO; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China.; State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen, Fujian 361005, China.; Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.; Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China. |
| Abstrakt: |
We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev 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)), semiempirical QM methods (e.g., AM1, PM3, OMx, and ODMx), and many types of ML 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 Δ-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 dozens of 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 a vast range of functionalities for surface hopping dynamics, such as fewest-switches surface hopping, to facilitate similar workflows via the Python API. |
