Related papers: Machine learning and excited-state molecular dynamics

Machine learning and excited-state molecular dynamics

URL: http://arxiv.org/abs/2005.14139v1
Date: Thu, 28 May 2020 16:43:18 GMT
Title: Machine learning and excited-state molecular dynamics
Authors: Julia Westermayr, Philipp Marquetand
Abstract summary: We survey advances for excited-state dynamics based on machine learning. We highlight successes, pitfalls, challenges and future avenues for machine learning approaches for light-induced molecular processes.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in their electronic ground state, the inclusion of light into the processes leads to electronically excited states and gives rise to several new challenges. Here, we survey recent advances for excited-state dynamics based on machine learning. In doing so, we highlight successes, pitfalls, challenges and future avenues for machine learning approaches for light-induced molecular processes.

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