Diffusion of electrically neutral radicals and anion radicals created by photochemical reactions

1998 ◽  
Vol 94 (2) ◽  
pp. 185-194 ◽  
Author(s):  
Koichi Okamoto ◽  
Noboru Hirota ◽  
and Masahide Terazima
Keyword(s):  
Photochemical Reactions ◽  
Anion Radicals

Column: Photochemical Reactions and Photophysical Processes

Polymer News ◽  
2004 ◽  
Vol 29 (6) ◽  
pp. 187-193 ◽  
Author(s):  
Pavol Hrdlovič
Keyword(s):  
Photochemical Reactions ◽  
Photophysical Processes

Column: Photochemical Reactions and Photophysical Processes

Polymer News ◽  
2005 ◽  
Vol 30 (10) ◽  
pp. 322-326
Author(s):  
Pavol Hrdlovic
Keyword(s):  
Photochemical Reactions ◽  
Photophysical Processes

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>

Photochemical and Electrochemical Reduction of Carbon Dioxide Catalyzed by a Polyoxometalate-Organic Photosensitizer Complex

2018 ◽  
Author(s):  
Chandan Dey ◽  
Ronny Neumann
Keyword(s):  
Carbon Dioxide ◽  
Cyclic Voltammetry ◽  
Formic Acid ◽  
Electrochemical Reduction ◽  
Mass Spectroscopy ◽  
Photochemical Reactions ◽  
Reducing Agent ◽  
Covalent Attachment ◽  
Hybrid Complex ◽  
Open Face

<p>A manganese substituted Anderson type polyoxometalate, [MnMo<sub>6</sub>O<sub>24</sub>]<sup>9-</sup>, tethered with an anthracene photosensitizer was prepared and used as catalyst for CO<sub>2</sub> reduction. The polyoxometalate-photosensitizer hybrid complex, obtained by covalent attachment of the sensitizer to only one face of the planar polyoxometalate, was characterized by NMR, IR and mass spectroscopy. Cyclic voltammetry measurements show a catalytic response for the reduction of carbon dioxide, thereby suggesting catalysis at the manganese site on the open face of the polyoxometalate. Controlled potentiometric electrolysis showed the reduction of CO<sub>2</sub> to CO with a TOF of ~15 sec<sup>-1</sup>. Further photochemical reactions showed that the polyoxometalate-anthracene hybrid complex was active for the reduction of CO<sub>2</sub> to yield formic acid and/or CO in varying amounts dependent on the reducing agent used. Control experiments showed that the attachment of the photosensitizer to [MnMo<sub>6</sub>O<sub>24</sub>]<sup>9-</sup> is necessary for photocatalysis.</p><div><br></div>

Excited State Tracking During the Relaxation of Coordination Compounds

2018 ◽  
Author(s):  
Juan Sanz García ◽  
Martial Boggio-Pasqua ◽  
Ilaria Ciofini ◽  
Marco Campetella
Keyword(s):  
Excited State ◽  
Excited States ◽  
Potential Energy Surfaces ◽  
Photochemical Reactions ◽  
Complex Nature ◽  
Electronic Excited States ◽  
Ruthenium Nitrosyl ◽  
State Tracking ◽  
Energy Surfaces ◽  
Electronic Wavefunction

<div>The ability to locate minima on electronic excited states (ESs) potential energy surfaces (PESs) both in the case of bright and dark states is crucial for a full understanding of photochemical reactions. This task has become a standard practice for small- to medium-sized organic chromophores thanks to the constant developments in the field of computational photochemistry. However, this remains a very challenging effort when it comes to the optimization of ESs of transition metal complexes (TMCs), not only due to the presence of several electronic excited states close in energy, but also due to the complex nature of the excited states involved. In this article, we present a simple yet powerful method to follow an excited state of interest during a structural optimization in the case of TMC, based on the use of a compact hole-particle representation of the electronic transition, namely the natural transition orbitals (NTOs). State tracking using NTOs is unambiguously accomplished by computing the mono-electronic wavefunction overlap between consecutive steps of the optimization. Here, we demonstrate that this simple but robust procedure works not only in the case of the cytosine but also in the case of the ES optimization of a ruthenium-nitrosyl complex which is very problematic with standard approaches.</div>

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