As we move towards the commercialization and upscaling of perovskite solar cells, it is essential to fabricate them in ambient environment rather than in the conventional glove box environment. The efficiency of ambient-processed perovskite solar cells lags behind those fabricated in controlled environments, primarily owing to external environmental factors such as humidity and temperature. In the case of device fabrication in ambient environments, relying solely on a single parameter, such as temperature or humidity, is insufficient for accurately characterizing environmental conditions. Therefore, the dew point is introduced as a parameter which accounts for both temperature and humidity. In this study, a machine learning model was developed to predict the efficiency of ambient-processed perovskite solar cells based on meteorological data, particularly the dew point. A total of 238 perovskite solar cells were fabricated, and their photovoltaic parameters and dew points were collected from March to December 2023. The collected data were used to train various tree-based machine learning models, with the random forest model achieving the highest accuracy. The efficiencies of the perovskite solar cells fabricated in January and February 2024 were predicted with a MAPE of 4.44%. An additional Shapley Additive exPlanations analysis confirmed the significance of the dew point in the performance of perovskite solar cells.

High-entropy alloys (HEAs) have attracted significant attention due to their exceptional physical, chemical, and mechanical properties. The current development of HEAs primarily depends on time-consuming and costly trial-and-error approaches, which not only hinder the efficient exploration of new compositions but also result in unnecessary resource and energy consumption, thereby negatively affecting sustainable development and production. To address this challenge, this study introduces a machine learning-based methodology for predicting the yield strengths of various HEA compositions. The model was trained using 181 data points and achieved an R² performance score of 0.85. To further assess its reliability and generalization capability, the model was validated using external data not included in the collected dataset. The validation was performed across four categories: modified Cantor alloys, refractory HEAs, eutectic HEAs, and other HEAs. The predicted yield strength trends were found to align with the actual experimental trends, demonstrating the model’s robust performance across various categories of HEAs. The proposed machine learning approach is expected to facilitate the combinatorial design of HEAs, thereby enabling efficient optimization of compositions and accelerating the development of novel alloys. Moreover, it has the potential to serve as a guideline for sustainable alloy design and environmentally conscious production in future HEA development.