Ruiqi Zhang is a PhD candidate whose research aims to develop novel solar cell technologies to support renewable energy. Specifically, he is devising machine learning (ML) algorithms to predict optimal structures for solar photovoltaic devices that utilize organic-metal-halide perovskites, a class of materials with promising optoelectronic properties for the next generation of solar photovoltaics. Supported by a MathWorks Fellowship, Ruiqi will pursue a research project with the aim of connecting the physical properties of perovskite materials to their operation in completed solar structures. MATLAB is an essential tool in all four phases of this project: photovoltaic device fabrication, data acquisition and processing, physical model development, and ML algorithm development. His predictive work has demonstrated less than 5% between predicted outputs and ground truth performance—a remarkable indicator of potential success. Ruiqi’s work promises to enable rapid, targeted investigations of various novel perovskite compositions and identify the optimal compositions for perovskite solar structures. Ultimately, his work could represent a major step forward in solar energy, helping to meet the need for clean, renewable power.