Density Estimation Based on Mixtures of Gaussians for Perovskite Solar Cells Modeling
F. Alexander Sepúlveda, Daniel Cerro-Ramos, T. Jesper Jacobsson
TL;DR
This paper introduces a probabilistic modeling approach using Gaussian Mixture Models to improve the design and optimization of perovskite solar cells.
Contribution
The novel contribution is a GMM-Assisted Optimization method that improves inverse design of solar cell synthesis conditions.
Findings
GMM-Assisted Optimization achieves an RMSE of 1.52 for predicting target power conversion efficiencies.
The method outperforms standard random-start optimization by more than halving the RMSE.
GMMs are shown to be effective for tasks like clustering, regression, and inverse design in perovskite solar cell research.
Abstract
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…
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Taxonomy
TopicsMachine Learning in Materials Science · Perovskite Materials and Applications · Gaussian Processes and Bayesian Inference