  A novel approach for calibrating quantum-chemical properties determined as
part of a high-throughput virtual screen to experimental analogs is presented.
Information on the molecular graph is extracted through the use of extended
connectivity fingerprints, and exploited using a Gaussian process to calibrate
both electronic properties such as frontier orbital energies, and optical gaps
and device properties such as short circuit current density, open circuit
voltage and power conversion efficiency. The Bayesian nature of this process
affords a value for uncertainty in addition to each calibrated value. This
allows the researcher to gain intuition about the model as well as the ability
to respect its bounds.
