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Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations

Li, Jingbai; Reiser, Patrick 1; Boswell, Benjamin R.; Eberhard, André 2; Burns, Noah Z.; Friederich, Pascal ORCID iD icon 1,2; Lopez, Steven A.
1 Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT)
2 Institut für Theoretische Informatik (ITI), Karlsruher Institut für Technologie (KIT)

Abstract:

Photochemical reactions are widely used by academic and industrial researchers to construct complex molecular architectures via mechanisms that often require harsh reaction conditions. Photodynamics simulations provide time-resolved snapshots of molecular excited-state structures required to understand and predict reactivities and chemoselectivities. Molecular excited-states are often nearly degenerate and require computationally intensive multiconfigurational quantum mechanical methods, especially at conical intersections. Non-adiabatic molecular dynamics require thousands of these computations per trajectory, which limits simulations to ∼1 picosecond for most organic photochemical reactions. Westermayr et al. recently introduced a neural-network-based method to accelerate the predictions of electronic properties and pushed the simulation limit to 1 ns for the model system, methylenimmonium cation (CH$_{2}$NH$_{2}$$^{+}$). We have adapted this methodology to develop the Python-based, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI$^{2}$MD) software for the cis–trans isomerization of trans-hexafluoro-2-butene and the 4π-electrocyclic ring-closing of a norbornyl hexacyclodiene. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000131877
Veröffentlicht am 27.04.2021
Originalveröffentlichung
DOI: 10.1039/d0sc05610c
Scopus
Zitationen: 16
Web of Science
Zitationen: 15
Dimensions
Zitationen: 17
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Nanotechnologie (INT)
Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2041-6520, 2041-6539
KITopen-ID: 1000131877
HGF-Programm 43.31.01 (POF IV, LK 01) Multifunctionality Molecular Design & Material Architecture
Erschienen in Chemical Science
Verlag Royal Society of Chemistry (RSC)
Band 12
Heft 14
Seiten 5302-5314
Nachgewiesen in Dimensions
Web of Science
Scopus
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