Müller C (2023)
Publication Language: English
Publication Type: Other publication type
Publication year: 2023
Edited Volumes: Bunsen-Magazin 2023
Series: Bunsen-Magazin 2023
Book Volume: Bunsen-Magazin 2023
Pages Range: 191-193
Journal Issue: 6
URI: https://bunsen.de/bmo/illuminating-photodynamics-with-machine-learning-techniques#c1529
DOI: 10.26125/0ehr-vk47
Open Access Link: https://bunsen.de/fileadmin/user_upload/media/Publikationen/BM_Open_Access/2023_BM_6_Illuminating-Photodynamics-with-Machine-Learning-Techniques.pdf
When molecules absorb light, they enter non-equilibrium states, triggering a cascade of nonadiabatic processes. Theoretical modeling of such photoinduced dynamics is pivotal for advancing research and innovation. Nevertheless, these simulations are constrained due to the resource-intensive aspects of quantum chemical methods. Machine learning (ML) offers a solution to this challenge. This article outlines how ML can accelerate and facilitate excited-state simulations.
APA:
Müller, C. (2023). Illuminating Photodynamics with Machine Learning Techniques.
MLA:
Müller, Carolin. Illuminating Photodynamics with Machine Learning Techniques. 2023.
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