Bayesian optimization with theory-based constraints accelerates search for stable photovoltaic perovskite materials (Papers Track)

Armi Tiihonen (Massachusetts Institute of Technology)

Abstract

Bringing a new photovoltaic technology from materials research stage to the market has historically taken decades, and the process has to be accelerated for increasing the share of renewables in energy production. We demonstrate Bayesian optimization for accelerating stability research. Convergence is reached even faster when using a constraint for integrating physical knowledge into the model. In our test case, we optimize the stability of perovskite compositions for perovskite solar cells, an efficient new solar cell technology suffering from limited lifetime of devices.