KIT | KIT-Bibliothek | Impressum | Datenschutz

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)


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
DOI: 10.1039/d0sc05610c
Zitationen: 39
Web of Science
Zitationen: 32
Zitationen: 43
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 Scopus
Web of Science
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page