The individual and combined effects of extrinsic (humidity and oxygen) and intrinsic (light, bias, and temperature) stressors on halide perovskite materials by implementing complementary optical and electrical characterization methods. These experiments are critical to assess the stability of the large variety of perovskite materials available for light-absorbing and -emitting applications. To pursue optimal ‘rest’ and ‘recovery’ conditions for device stable operation, we propose the implementation of a machine learning approach based on supervised learning upon exposing the samples to distinct values of humidity, oxygen, illumination, bias, and temperature, which will be discussed in details.
|