Seminar by Dr Michal Krompiec from Merck Chemicals – Simulation of Organic Photovoltaics.

Seminar by Dr Michal Krompiec from Merck Chemicals – Simulation of Organic Photovoltaics.

Dr Michal Krompiec, research scientist at Merck Chemicals, gave an interesting seminar on the topic of organic photovoltaics (OPV), first highlighting the capabilities of this relatively new technology and then discussing about how computer simulations are an essential and powerful tool for the design of high performance materials in order to improve the quality of these devices.

In the first part of the seminar the advantages of OPV technology were explored, such as:

  • Efficiency under low and/or diffuse light;
  • Efficiency on both sides of the device;
  • Low capex (i.e. capital expenditure) roll-to-roll printing in production, which makes it a good investment, since the benefit continues over a long period of time.

On the other hand Dr Krompiec also talked about the drawbacks of OPV: the average efficiency of these devices is not yet comparable with the one of the standard silicon-based modules and moreover it sensibly decreases when going from a laboratory scale (single cell) to a production scale (modules assembled with more than one cell). To tackle these issues computer simulations are needed, first because laboratory tests would be too expensive and then because some properties cannot be experimentally calculated with an adequate accuracy.

Afterwards Dr Krompiec emphasized the importance of the so-called Scharber’s model when designing new materials: it relates material properties to potential maximum device efficiency and the three main quantities of interest (ionization potential, electronic affinity and energy gap) can be experimentally measured or simulated.

The core (i.e. the active layer) of an OPV module consists of a blend of polymer (electron donor) and fullerene (electron acceptor) and most of the computer simulations focus on these two species. A case in point, as mentioned by Dr Krompiec, is the Harvard Clean Energy Project, probably the largest and (computationally) costly computational chemistry project published so far and a clear example of machine learning: the database contains data on about 2.3 million polymer structures, derived from 150 million DFT (Density Functional Theory) calculations. Simulations were run on all of these possible candidates in order to calculate the electronic properties and therefore understand which polymers are potentially the most promising in terms of efficiency.

Dr Krompiec finished his talk highlighting the interesting research opportunities of OPV modelling, such as the application of novel QM methods in order to get an insight into supramolecular interactions or multiscale approaches, making OPV a stimulating field of study.

Posted by Gabriele Boschetto