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      Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics

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          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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          Author and article information

          Journal
          17 February 2020
          Article
          2002.07264
          b1ae16cc-3918-4a0e-b0b0-07f27510f5d1

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          physics.chem-ph cs.LG stat.ML

          Machine learning,Physical chemistry,Artificial intelligence
          Machine learning, Physical chemistry, Artificial intelligence

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