It’s All About That Bayes: Data-Driven Insights into Energy Devices without the Black Box
DescriptionLarge black-box models such as neural networks are exciting and powerful tools, but they’re not the only way to learn from data! I will demonstrate Bayesian parameter estimation to extract unprecedented insights from simple electrical characterization of photovoltaic devices. By running a physical model many times, spanning the space of properties we wish to fit, then comparing results to the physical measurements, we obtain a posterior distribution over the properties of interest. This approach has numerous advantages. First, with the democratization of computational power, the tradeoff between the researcher time/labor to directly measure these properties and the computational effort to run many simulations is increasingly favorable. Second, not only are the inferred values of comparable accuracy (and sometimes superior precision!) to direct physical probes, we can also be confident that they represent the most performance-relevant information about those properties, because we’ve measured them in the device context, rather than in a specially prepared sample that may have unrepresentative characteristics and measurement conditions. I will demonstrate the ability not only to extract properties of both bulk materials and interfaces, but to easily observe relationships between them. Finally, I will discuss ongoing work in utilizing this approach to directly inform/guide device engineering.
TimeTuesday, June 2712:00 - 12:30 CEST
Session Chair
Event Type
Chemistry and Materials