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MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning

Lina Zhang1, Sebastian V Pios2, Mikołaj Martyka3

  • 1College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China.

Journal of Chemical Theory and Computation
|June 5, 2024
PubMed
Summary
This summary is machine-generated.

We introduce MLatom@XACS, an open-source software for simulating nonadiabatic dynamics using machine learning and quantum mechanics. It efficiently reproduces molecular isomerization yields, enabling rapid analysis of complex chemical reactions.

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Area of Science:

  • Computational Chemistry
  • Chemical Physics
  • Materials Science

Background:

  • Simulating nonadiabatic dynamics is crucial for understanding photochemical reactions.
  • Existing methods often require significant computational resources.
  • Integrating quantum mechanics (QM) and machine learning (ML) offers a promising avenue for efficient simulations.

Purpose of the Study:

  • To present MLatom@XACS, an open-source software ecosystem for on-the-fly surface hopping nonadiabatic dynamics.
  • To provide a flexible platform combining various QM and ML methods.
  • To demonstrate the efficiency and accuracy of the developed AIQM1 model.

Main Methods:

  • Implementation of the Landau-Zener-Belyaev-Lebedev algorithm for surface hopping.
  • Support for diverse QM methods (ab initio and semiempirical) and ML potentials (KREG, ANI, MACE).
  • Development of the AIQM1 model based on Δ-learning for cost-effective dynamics.

Main Results:

  • MLatom@XACS enables on-the-fly nonadiabatic dynamics simulations via a Python API.
  • The AIQM1 model accurately reproduces the isomerization quantum yield of trans-azobenzene at low computational cost.
  • Example scripts facilitate geometry optimization, initial condition sampling, and population analysis.

Conclusions:

  • MLatom@XACS provides a versatile and efficient tool for nonadiabatic dynamics.
  • The ecosystem facilitates the development and application of ML models for chemical dynamics.
  • Future integration with Newton-X will expand its capabilities for advanced surface hopping methods.