Transfer Learned Designer Polymers For Organic Solar Cells

Organic photovoltaic (OPV) materials
have been examined extensively
over the past two decades for solar cell applications because of the
potential for device flexibility, low-temperature solution processability,
and negligible environmental impact. However, discovery of new candidate
OPV materials, especially polymer-based electron donors, that demonstrate
notable power conversion efficiencies (PCEs), is nontrivial and time-intensive
exercise given the extensive set of possible chemistries. Recent progress
in machine learning accelerated materials discovery has facilitated
to address this challenge, with molecular line representations, such
as Simplified Molecular-Input Line-Entry Systems (SMILES), gaining
popularity as molecular fingerprints describing the donor chemical
structures. Here, we employ a transfer learning based recurrent neural
(LSTM) model, which harnesses the SMILES molecular fingerprints as
an input to generate novel designer chemistries for OPV devices. The
generative model, perfected on a small focused OPV data set, predicts
new polymer repeat units with potentially high PCE. Calculations of
the similarity coefficient between the known and the generated polymers
corroborate the accuracy of the model predictability as a function
of the underlying chemical specificity. The data-enabled framework
is sufficiently generic for use in accelerated machine learned materials
discovery for various chemistries and applications, mining the hitherto
available experimental and computational data.