  Defect-assisted recombination processes are critical to understand, as they
frequently limit photovoltaic (PV) device performance. However, the physical
parameters governing these processes can be extremely challenging to measure,
requiring specialized techniques and sample preparation. And yet the fact that
they limit performance as measured by current-voltage (JV) characterization
indicates that they must have some detectable signal in that measurement. In
this work, we use numerical device models that explicitly account for these
parameters with high-throughput JV measurements and Bayesian inference to
construct probability distributions over recombination parameters, showing the
ability to recover values consistent with previously-reported literature
measurements. The Bayesian approach enables easy incorporation of data and
models from other sources; we demonstrate this with temperature dependence of
carrier capture cross-sections. The ability to extract these fundamental
physical parameters from standardized, automated measurements on completed
devices is promising for both established industrial PV technologies and newer
research-stage ones.
