Chemistry of organosilicon compounds. 93. New photochemical reactions of vinyldisilanes through silaethene or silacyclopropane intermediates

1976 ◽  
Vol 98 (23) ◽  
pp. 7424-7425 ◽  
Author(s):  
H. Sakurai ◽  
Y. Kamiyama ◽  
Y. Nakadaira
Keyword(s):  
Photochemical Reactions ◽  
Organosilicon Compounds

ChemInform Abstract: Photochemical Reactions of Organosilicon Compounds

ChemInform ◽  
2010 ◽  
Vol 22 (40) ◽  
pp. no-no
Author(s):  
V. JANCOVICOVA ◽  
D. MARIANIOVA ◽  
D. BAKOS ◽  
L. LAPCIK
Keyword(s):  
Photochemical Reactions ◽  
Organosilicon Compounds

Chemistry of organosilicon compounds. 303. The missing hexasilaprismane: synthesis, x-ray analysis and photochemical reactions

1993 ◽  
Vol 115 (13) ◽  
pp. 5853-5854 ◽  
Author(s):  
Akira Sekiguchi ◽  
Tetsuo Yatabe ◽  
Chizuko Kabuto ◽  
Hideki Sakurai
Keyword(s):  
Photochemical Reactions ◽  
Organosilicon Compounds ◽  
X Ray

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

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