Minimal Input, Maximum Insight: Inferring Material Parameters From Solar Cell JV Curves Alone.

A key challenge in the development of materials for the next generation of solar cells, sensors and transistors is linking macroscopic device performance to underlying microscopic properties. For years, fabrication of devices has been faster than our ability to characterize them. This has led to a random walk of material development, with new materials being proposed faster than our understanding. We present two neural network-based methods for extracting key material parameters, including charge carrier mobility and trap state density, in optoelectronic devices such as solar cells. Our methods require only a single measured light current-voltage curve and modest computational resources, making our approach applicable in even minimally equipped laboratories. Unlike traditional machine learning models, our methods place the final material values in a non-Gaussian likelihood distribution, allowing confidence assessment of each predicted parameter.We demonstrate these techniques on freshly fabricated PM6:Y12 and PM6:BTP-eC9 organic solar cells, and then track a single PM6:BTP-eC9 device as it degrades in air, recovering the evolution of carrier lifetime, mobility and shunt resistance. This approach enables rapid, low-cost extraction of key material parameters from simple JV measurements alone, providing a practical route to accelerate optimisation of next-generation solar-energy materials and devices.