scholarly journals Machine learning for quantum dynamics: deep learning of excitation energy transfer properties

2017 ◽  
Vol 8 (12) ◽  
pp. 8419-8426 ◽  
Author(s):  
Florian Häse ◽  
Christoph Kreisbeck ◽  
Alán Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Energy Transfer ◽  
Deep Learning ◽  
Excitation Energy ◽  
Quantum Dynamics ◽  
Light Harvesting ◽  
Excitation Energy Transfer ◽  
Harvesting Systems ◽  
The Relationship ◽  
Transfer Properties

Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics.

Separating Annihilation and Excitation Energy Transfer Dynamics in Light Harvesting Systems

2013 ◽  
Vol 117 (38) ◽  
pp. 11372-11382 ◽  
Author(s):  
Mikas Vengris ◽  
Delmar S. Larsen ◽  
Leonas Valkunas ◽  
Gerdenis Kodis ◽  
Christian Herrero ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Excitation Energy ◽  
Light Harvesting ◽  
Excitation Energy Transfer ◽  
Harvesting Systems

scholarly journals Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics

2021 ◽  
Author(s):  
Arif Ullah ◽  
Pavlo O. Dral
Keyword(s):  
Artificial Intelligence ◽  
Energy Transfer ◽  
Excitation Energy ◽  
Quantum Dynamics ◽  
Light Harvesting ◽  
Computational Cost ◽  
Excitation Energy Transfer ◽  
Reorganization Energy ◽  
Quantum Effects ◽  
Light Harvesting Complexes

Exploring excitation energy transfer (EET) in light-harvesting complexes (LHCs) is essential for understanding the natural processes and design of highly-efficient photovoltaic devices. LHCs are open systems, where quantum effects may play a crucial role for almost perfect utilization of solar energy. Simulation of energy transfer with inclusion of quantum effects can be done within the framework of dissipative quantum dynamics (QD), which are computationally expensive. Thus, artificial intelligence (AI) offers itself as a tool for reducing the computational cost. We suggest AI-QD approach using AI to directly predict QD as a function of time and other parameters such as temperature, reorganization energy, etc., completely circumventing the need of recursive step-wise dynamics propagation in contrast to the traditional QD and alternative, recursive AI-based QD approaches. Our trajectory-learning AI-QD approach is able to predict the correct asymptotic behavior of QD at infinite time. We demonstrate AI-QD on seven-sites Fenna–Matthews–Olson (FMO) complex.

Organogels as scaffolds for excitation energy transfer and light harvesting

2008 ◽  
Vol 37 (1) ◽  
pp. 109-122 ◽  
Author(s):  
Ayyappanpillai Ajayaghosh ◽  
Vakayil K. Praveen ◽  
Chakkooth Vijayakumar
Keyword(s):  
Energy Transfer ◽  
Excitation Energy ◽  
Light Harvesting ◽  
Excitation Energy Transfer
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