Time-dependent quantum dynamical simulations of C2condensation under extreme conditions

2012 ◽  
Vol 14 (18) ◽  
pp. 6273-6279 ◽  
Author(s):  
Jacek Jakowski ◽  
Stephan Irle ◽  
Keiji Morokuma
Keyword(s):  
Time Dependent ◽  
Extreme Conditions ◽  
Quantum Dynamical ◽  
Dynamical Simulations

Quantum Dynamical Simulations as a Tool for Predicting Photoinjection Mechanisms in Dye-Sensitized TiO2 Solar Cells

2012 ◽  
Vol 3 (18) ◽  
pp. 2548-2555 ◽  
Author(s):  
M. Belén Oviedo ◽  
Ximena Zarate ◽  
Christian F. A. Negre ◽  
Eduardo Schott ◽  
Ramiro Arratia-Pérez ◽  
...  
Keyword(s):  
Solar Cells ◽  
Dye Sensitized ◽  
Quantum Dynamical ◽  
Dynamical Simulations

scholarly journals Intramolecular Singlet Fission: Insights from Quantum Dynamical Simulations

2018 ◽  
Vol 9 (20) ◽  
pp. 5979-5986 ◽  
Author(s):  
S. Rajagopala Reddy ◽  
Pedro B. Coto ◽  
Michael Thoss
Keyword(s):  
Singlet Fission ◽  
Quantum Dynamical ◽  
Dynamical Simulations

Time-Dependent Computational Methods for Matter Under Extreme Conditions

2014 ◽  
pp. 195-214
Author(s):  
Barry I. Schneider ◽  
Klaus R. Bartschat ◽  
Xiaoxu Guan ◽  
David Feder ◽  
Lee A. Collins
Keyword(s):  
Computational Methods ◽  
Time Dependent ◽  
Extreme Conditions

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 Kinetics of Materials at Extreme Conditions: Understanding the Time Dependent Approach to Equilibrium at MaRIE

2017 ◽  
Author(s):  
R. G. Kraus ◽  
D. Mcnabb ◽  
M. Kumar ◽  
J. Eggert ◽  
J. Borg ◽  
...  
Keyword(s):  
Time Dependent ◽  
Extreme Conditions ◽  
Approach To Equilibrium ◽  
Kinetics Of
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