A Framework to Pinpoint Bottlenecks in Emerging Solar Cells and Disordered Devices via Differential Machine Learning

TL;DR

This paper introduces neural network-based methods to identify microscopic material parameters from device performance data, enabling better understanding and optimization of emerging solar cells and disordered devices.

Contribution

The authors develop two neural network techniques that extract key material parameters from simple measurements, providing confidence estimates and applicable with minimal computational resources.

Findings

Successfully applied to organic solar cells, demonstrating accurate parameter extraction.

Provides confidence intervals for predictions, enhancing reliability.

Applicable to various optoelectronic devices with limited data.

Abstract

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 solely 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 using fresh PM6:Y12 and degraded PM6:BTP-eC9 organic solar cells.