Density Estimation Based on Mixtures of Gaussians for Perovskite Solar Cells Modeling

Accurately modeling the complex relationships among synthesis parameters, material compositions, and performance metrics is essential for accelerating the development of perovskite solar cells (PSCs). In this context, machine learning (ML) has proven to be a valuable tool. While most ML applications in PSC research rely on discriminative "black-box" models, this study adopts a generative approach by modeling the joint probability density function. We employ Gaussian Mixture Models (GMMs), a pragmatic and interpretable choice well-suited for the scarce, low-dimensional tabular data typical of PSC research. This single GMM framework is evaluated on five distinct tasks: discovering clusters, regression, generating novel configurations, training on data sets with missing data and, inverse design of the experimental (synthesis) conditions. That is, assuming we have the perovskite material composition and a target PCE, we infer the experimental conditions. For this latter task we use a novel "GMM-Assisted Optimization" method, which demonstrates to be more effective than standard random-start optimization, achieving an RMSE of 1.52 against target PCEs, more than halving the 3.32 RMSE of the baseline. These findings highlight the power of probabilistic modeling for data-driven discovery in PSC research.