Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin-Film Solar Cells

Abstract

There is currently a worldwide effort to develop materials for solar energy harvesting which are efficient and cost effective, and do not emit significant levels of CO₂ during manufacture. When a researcher fabricates a novel device from a novel material system, it often takes many weeks of experimental effort and data analysis to understand why any given device/material combination produces an efficient or poorly optimized cell. It therefore takes the community tens of years to transform a promising material system to a fully optimized cell ready for production (perovskites are a contemporary example). Herein, developed is a new and rapid approach to understanding device/material performance, which uses a combination of machine learning, device modeling, and experiment. Providing a set of electrical device parameters (charge carrier mobilities, recombination rates, trap densities, etc.) in a matter of seconds thus offers a fast way to directly link fabrication conditions to device/material performance, pointing a way to further and more rapid optimization of light harvesting devices. The method is demonstrated by using it to understand annealing temperature and surfactant choice and in terms of charge carrier dynamics in organic solar cells made from the P3HT:PCBM, PBTZT-stat-BDTT-8:PCBM, and PTB7:PCBM material systems.