Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion

Repeatable Perovskite Solar Cells through Fully Automated Spin-Coating and Quenching

Enhancing reproducibility, repeatability, as well as facilitating transferability between laboratories will accelerate the progress in many material domains, wherein perovskite-based optoelectronics are a prime use case. This study presents fully automated perovskite thin film processing using a commercial spin-coating robot in an inert atmosphere. We successfully apply this novel processing method to antisolvent quenching. This process is typically difficult to reproduce and transfer and is now enhanced to exceptional repeatability in comparison to manual processing. Champion perovskite solar cells demonstrate power conversion efficiencies as high as 19.9%, proving the transferability of established manual spin-coating processes to automatic setups. Comparison with human experts reveals that the performance is already on par, while automated processing yields improved homogeneity across the substrate surface. This work demonstrates that fully automated perovskite thin film processing improves repeatability. Such systems bear the potential to become a foundation for autonomous optimization and greatly improve transferability between laboratories.

Improving SDG Classification Precision Using Combinatorial Fusion

Combinatorial fusion algorithm (CFA) is a machine learning and artificial intelligence (ML/AI) framework for combining multiple scoring systems using the rank-score characteristic (RSC) function and cognitive diversity (CD). When measuring the relevance of a publication or document with respect to the 17 Sustainable Development Goals (SDGs) of the United Nations, a classification scheme is used. However, this classification process is a challenging task due to the overlapping goals and contextual differences of those diverse SDGs. In this paper, we use CFA to combine a topic model classifier (Model A) and a semantic link classifier (Model B) to improve the precision of the classification process. We characterize and analyze each of the individual models using the RSC function and CD between Models A and B. We evaluate the classification results from combining the models using a score combination and a rank combination, when compared to the results obtained from human experts. In summary, we demonstrate that the combination of Models A and B can improve classification precision only if these individual models perform well and are diverse.