  Organic Solar Cells are a promising technology for solving the clean energy
crisis in the world. However, generating candidate chemical compounds for solar
cells is a time-consuming process requiring thousands of hours of laboratory
analysis. For a solar cell, the most important property is the power conversion
efficiency which is dependent on the highest occupied molecular orbitals (HOMO)
values of the donor molecules. Recently, machine learning techniques have
proved to be very useful in building predictive models for HOMO values of donor
structures of Organic Photovoltaic Cells (OPVs). Since experimental datasets
are limited in size, current machine learning models are trained on data
derived from calculations based on density functional theory (DFT). Molecular
line notations such as SMILES or InChI are popular input representations for
describing the molecular structure of donor molecules. The two types of line
representations encode different information, such as SMILES defines the bond
types while InChi defines protonation. In this work, we present an ensemble
deep neural network architecture, called SINet, which harnesses both the SMILES
and InChI molecular representations to predict HOMO values and leverage the
potential of transfer learning from a sizeable DFT-computed dataset- Harvard
CEP to build more robust predictive models for relatively smaller HOPV
datasets. Harvard CEP dataset contains molecular structures and properties for
2.3 million candidate donor structures for OPV while HOPV contains DFT-computed
and experimental values of 350 and 243 molecules respectively. Our results
demonstrate significant performance improvement from the use of transfer
learning and leveraging both molecular representations.
