Screening of Polymer Solar Cell Materials with Machine Learning

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Searching for new and better materials for polymer solar cells is a computationally costly affair using density functional theory calculations. In this work we propose a screening procedure using a simple string representation for a promising class of donor-acceptor polymers in conjunction with a grammar variational autoencoder.
[The polymer materials database from this work can be browsed here]
[Journal article]
MAY 2018

Modeling donor-Acceptor Polymers for organic photovoltaics

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We built a tight-binding based method to calculate quantities like molecular-orbital energies, ionization potential, electron affinity, and lowest photo absorption energy. The DFT is used to obtain quantum mechanical parameters of the TB model.
[A small polymer material database from this work can be browsed here]
JAN 2018

PhD Thesis

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Steady-state and time-dependent responses of organic devices to an applied voltage caused by the electronic processes taking place in the disordered organic semiconductors.
March 2015

Towards predictive modeling of OLEDs

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Modeling the complex processes in OLEDs on a molecular scale allows manufacturers to greatly improve their OLED design processes and reduce the cost. At the same time the energy efficiency and lifetime of OLEDs can be increased... read more at phys.org
April 2013

About OLEDs and modeling

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Snapshot from a kinetic Monte Carlo simulation of a multilayer white OLED. Orange and black spheres denote injected holes and electrons; red green, and blue radiative excitons in corresponding emitting layers.
Nature Mater. 12 652 (2013)

OLEDs are energy efficient, large area light sources with mechanical flexibility. An OLED is made of several layers of organic material placed between two electrodes ... read more
April 2013