Ultrafast non-adiabatic dynamics of excited diphenylmethyl bromide elucidated by quantum dynamics and semi-classical on-the-fly dynamics

2018 ◽  
Vol 20 (35) ◽  
pp. 22753-22761
Author(s):  
Franziska Schüppel ◽  
Matthias K. Roos ◽  
Regina de Vivie-Riedle
Keyword(s):  
Quantum Dynamics ◽  
Reaction Dynamics ◽  
Quantum Dynamical ◽  
Adiabatic Dynamics

Quantum dynamical and semi-classical investigations explain the reaction dynamics and the experimentally observed wavepacket motion during ultrafast photodissociation of diphenylmethylbromide.

scholarly journals Full-dimensional quantum stereodynamics of the non-adiabatic quenching of OH(A2Σ+) by H2

2021 ◽  
Author(s):  
Bin Zhao ◽  
Shanyu Han ◽  
Christopher L. Malbon ◽  
Uwe Manthe ◽  
David. R. Yarkony ◽  
...  
Keyword(s):  
Quantum Dynamics ◽  
Previous Analysis ◽  
Branching Ratio ◽  
Energy Matrix ◽  
Elastic Channel ◽  
Electronically Excited ◽  
Oppenheimer Approximation ◽  
Electronic Motion ◽  
Adiabatic Dynamics ◽  
Good Agreement

AbstractThe Born–Oppenheimer approximation, assuming separable nuclear and electronic motion, is widely adopted for characterizing chemical reactions in a single electronic state. However, the breakdown of the Born–Oppenheimer approximation is omnipresent in chemistry, and a detailed understanding of the non-adiabatic dynamics is still incomplete. Here we investigate the non-adiabatic quenching of electronically excited OH(A2Σ+) molecules by H2 molecules using full-dimensional quantum dynamics calculations for zero total nuclear angular momentum using a high-quality diabatic-potential-energy matrix. Good agreement with experimental observations is found for the OH(X2Π) ro-vibrational distribution, and the non-adiabatic dynamics are shown to be controlled by stereodynamics, namely the relative orientation of the two reactants. The uncovering of a major (in)elastic channel, neglected in a previous analysis but confirmed by a recent experiment, resolves a long-standing experiment–theory disagreement concerning the branching ratio of the two electronic quenching channels.

Quantum calculations of the photoelectron spectra of the OH−·NH3 anion: implications for OH + NH3 → H2O + NH2 reaction dynamics

2021 ◽  
Vol 23 (11) ◽  
pp. 6950-6958
Author(s):  
Kohei Saito ◽  
Yutaro Sugiura ◽  
Takaaki Miyazaki ◽  
Yukinobu Takahashi ◽  
Toshiyuki Takayanagi
Keyword(s):  
Quantum Dynamics ◽  
Photoelectron Spectrum ◽  
Reaction Dynamics ◽  
Photoelectron Spectra ◽  
Quantum Calculations ◽  
Anion Complex

Quantum dynamics calculations were performed to analyze the experimentally measured photoelectron spectrum of the OH−·NH3 anion complex.

Quantum Dynamics of Exciton Transport and Dissociation in Multichromophoric Systems

2021 ◽  
Vol 72 (1) ◽  
pp. 591-616 ◽  
Author(s):  
Wjatscheslaw Popp ◽  
Dominik Brey ◽  
Robert Binder ◽  
Irene Burghardt
Keyword(s):  
Charge Transfer ◽  
Quantum Dynamics ◽  
Low Frequency ◽  
Functional Materials ◽  
Phonon Modes ◽  
Quantum Dynamical ◽  
Exciton Migration ◽  
Exciton Transport ◽  
Charge Transfer Excitons ◽  
Vibronic Couplings

Due to the subtle interplay of site-to-site electronic couplings, exciton delocalization, nonadiabatic effects, and vibronic couplings, quantum dynamical studies are needed to elucidate the details of ultrafast photoinduced energy and charge transfer events in organic multichromophoric systems. In this vein, we review an approach that combines first-principles parameterized lattice Hamiltonians with accurate quantum dynamical simulations using advanced multiconfigurational methods. Focusing on the elementary transfer steps in organic functional materials, we address coherent exciton migration and creation of charge transfer excitons in homopolymers, notably representative of the poly(3-hexylthiophene) material, as well as exciton dissociation at polymer:fullerene heterojunctions. We emphasize the role of coherent transfer, trapping effects due to high-frequency phonon modes, and thermal activation due to low-frequency soft modes that drive a diffusive dynamics.

scholarly journals Excited state non-adiabatic dynamics of pyrrole: A time-resolved photoelectron spectroscopy and quantum dynamics study

2015 ◽  
Vol 142 (7) ◽  
pp. 074302 ◽  
Author(s):  
Guorong Wu ◽  
Simon P. Neville ◽  
Oliver Schalk ◽  
Taro Sekikawa ◽  
Michael N. R. Ashfold ◽  
...  
Keyword(s):  
Excited State ◽  
Photoelectron Spectroscopy ◽  
Quantum Dynamics ◽  
Time Resolved ◽  
Adiabatic Dynamics

scholarly journals Excited state non-adiabatic dynamics of N-methylpyrrole: A time-resolved photoelectron spectroscopy and quantum dynamics study

2016 ◽  
Vol 144 (1) ◽  
pp. 014309 ◽  
Author(s):  
Guorong Wu ◽  
Simon P. Neville ◽  
Oliver Schalk ◽  
Taro Sekikawa ◽  
Michael N. R. Ashfold ◽  
...  
Keyword(s):  
Excited State ◽  
Photoelectron Spectroscopy ◽  
Quantum Dynamics ◽  
Time Resolved ◽  
Adiabatic Dynamics

scholarly journals Optical spectrum, perceived color, refractive index, and non-adiabatic dynamics of the photochromic diarylethene CMTE

2014 ◽  
Vol 16 (28) ◽  
pp. 14531-14538 ◽  
Author(s):  
Christian Wiebeler ◽  
Christina A. Bader ◽  
Cedrik Meier ◽  
Stefan Schumacher
Keyword(s):  
Molecular Dynamics ◽  
Refractive Index ◽  
Optical Absorption ◽  
Ab Initio ◽  
Optical Spectrum ◽  
Reaction Dynamics ◽  
Adiabatic Dynamics ◽  
Perceived Color ◽  
Comprehensive Study

A comprehensive study of the photochromic diarylethene CMTE is presented, including optical absorption, perceived color, refractive index, and reaction dynamics with non-adiabatic ab initio molecular dynamics.

scholarly journals Harnessing Deep Neural Networks to Solve Inverse Problems in Quantum Dynamics: Machine-Learned Predictions of Time-Dependent Optimal Control Fields

2020 ◽  
Author(s):  
Xian Wang ◽  
Anshuman Kumar ◽  
Christian Shelton ◽  
Bryan Wong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Optimal Control ◽  
Quantum Dynamics ◽  
Deep Neural Networks ◽  
Time Dependent ◽  
Learning Approaches ◽  
Quantum Dynamical ◽  
Quantum Dynamical Systems

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, <i>E(t)</i>, that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of <i>E(t)</i>), and (2) a cross-correlation neural network approach for directly predicting <i>E(t)</i> in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning approaches, particularly deep neural networks, can be employed as a cost-effective statistical approach for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.

scholarly journals Quantum interference between spin-orbit split partial waves in the F + HD → HF + D reaction

Science ◽  
2021 ◽  
Vol 371 (6532) ◽  
pp. 936-940 ◽  
Author(s):  
Wentao Chen ◽  
Ransheng Wang ◽  
Daofu Yuan ◽  
Hailin Zhao ◽  
Chang Luo ◽  
...  
Keyword(s):  
Electron Spin ◽  
Cross Sections ◽  
Quantum Interference ◽  
Quantum Dynamics ◽  
Reaction Dynamics ◽  
Spin Orbit ◽  
Resolution Imaging ◽  
Spin Orbit Interactions ◽  
Shaped Pattern ◽  
Orbit Characteristics

The effect of electron spin-orbit interactions on chemical reaction dynamics has been a topic of much research interest. Here we report a combined experimental and theoretical study on the effect of electron spin and orbital angular momentum in the F + HD → HF + D reaction. Using a high-resolution imaging technique, we observed a peculiar horseshoe-shaped pattern in the product rotational-state–resolved differential cross sections around the forward-scattering direction. The unusual dynamics pattern could only be explained properly by highly accurate quantum dynamics theory when full spin-orbit characteristics were considered. Theoretical analysis revealed that the horseshoe pattern was largely the result of quantum interference between spin-orbit split–partial-wave resonances with positive and negative parities, providing a distinctive example of how spin-orbit interaction can effectively influence reaction dynamics.

scholarly journals Born–Oppenheimer and Renner–Teller coupled-channel quantum reaction dynamics of O(3P) + H2+(X2Σg+) collisions

2015 ◽  
Vol 17 (36) ◽  
pp. 23392-23402 ◽  
Author(s):  
Pablo Gamallo ◽  
Paolo Defazio ◽  
Miguel González ◽  
Miguel Paniagua ◽  
Carlo Petrongolo
Keyword(s):  
Quantum Dynamics ◽  
Reaction Dynamics ◽  
Time Dependent ◽  
Quantum Reaction ◽  
Coupled Channel

We present Born–Oppenheimer (BO) and Renner–Teller (RT) time dependent quantum dynamics studies of the reactions O(3P) + H2+(X2Σg+) → OH+(X3Σ−) + H(2S) and OH(X2Π) + H+.

scholarly journals Harnessing Deep Neural Networks to Solve Inverse Problems in Quantum Dynamics: Machine-Learned Predictions of Time-Dependent Optimal Control Fields

2020 ◽  
Author(s):  
Xian Wang ◽  
Anshuman Kumar ◽  
Christian Shelton ◽  
Bryan Wong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Optimal Control ◽  
Quantum Dynamics ◽  
Deep Neural Networks ◽  
Time Dependent ◽  
Learning Approaches ◽  
Quantum Dynamical ◽  
Quantum Dynamical Systems

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, <i>E(t)</i>, that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of <i>E(t)</i>), and (2) a cross-correlation neural network approach for directly predicting <i>E(t)</i> in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning approaches, particularly deep neural networks, can be employed as a cost-effective statistical approach for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.

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