Combining SchNet and SHARC: The SchNarc machine learning approach for
excited-state dynamics
- URL: http://arxiv.org/abs/2002.07264v1
- Date: Mon, 17 Feb 2020 21:38:35 GMT
- Title: Combining SchNet and SHARC: The SchNarc machine learning approach for
excited-state dynamics
- Authors: Julia Westermayr, Michael Gastegger, Philipp Marquetand
- Abstract summary: We show how deep learning can be used to advance the research field of photochemistry.
The properties are multiple energies, forces, nonadiabatic couplings and spin-orbit couplings.
In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on a model system and two realistic polyatomic molecules.
- Score: 3.058685580689605
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, deep learning has become a part of our everyday life and is
revolutionizing quantum chemistry as well. In this work, we show how deep
learning can be used to advance the research field of photochemistry by
learning all important properties for photodynamics simulations. The properties
are multiple energies, forces, nonadiabatic couplings and spin-orbit couplings.
The nonadiabatic couplings are learned in a phase-free manner as derivatives of
a virtually constructed property by the deep learning model, which guarantees
rotational covariance. Additionally, an approximation for nonadiabatic
couplings is introduced, based on the potentials, their gradients and Hessians.
As deep-learning method, we employ SchNet extended for multiple electronic
states. In combination with the molecular dynamics program SHARC, our approach
termed SchNarc is tested on a model system and two realistic polyatomic
molecules and paves the way towards efficient photodynamics simulations of
complex systems.
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