Feature Selection in Machine Learning for Perovskite Materials Design and Discovery

Abstract

Perovskite materials have been one of the most important research objects in materials science due to their excellent photoelectric properties as well as correspondingly complex structures. Machine learning (ML) methods have been playing an important role in the design and discovery of perovskite materials, while feature selection as a dimensionality reduction method has occupied a crucial position in the ML workflow. In this review, we introduced the recent advances in the applications of feature selection in perovskite materials. First, the development tendency of publications about ML in perovskite materials was analyzed, and the ML workflow for materials was summarized. Then the commonly used feature selection methods were briefly introduced, and the applications of feature selection in inorganic perovskites, hybrid organic-inorganic perovskites (HOIPs), and double perovskites (DPs) were reviewed. Finally, we put forward some directions for the future development of feature selection in machine learning for perovskite material design.

A data-driven QSPR model for screening organic corrosion inhibitors for carbon steel using machine learning techniques

“…Conversely, when the number of top descriptors exceeds 80, a notable decline in the model's predictive performance is observed, suggesting the potential inclusion of descriptors unrelated to IE in the dataset, leading to over-fitting. 35,36 The model achieves its optimal predictive performance with approximately 40 descriptors. These findings align with the result of Li et al , who employed the recursive feature elimination method to select descriptors for an IE predictive model on an Mg alloy.…”

“…to over-tting. 35,36 The model achieves its optimal predictive performance with approximately 40 descriptors. These ndings align with the result of Li et al, who employed the recursive feature elimination method to select descriptors for an IE predictive model on an Mg alloy.…”

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

“…126103-4 Feature selection plays a crucial role in data analysis and ML, which can improve the performance and interpretability of models. Wang et al [75] introduced standard feature selection methods for perovskite materials. Whether used individually or in combination, the Pearson correlation coefficient in the filtering method, recursive feature elimination in the wrapping method, and tree model in the embedding method appeared more frequently.…”

Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr3 Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries

“…The excellent carrier properties of perovskite materials and their crystal structure susceptible to polar molecules make perovskites very potential to be used as room-temperature gas sensors. − At the same time, perovskite is very easy to coordinate with ester groups, so perovskite has the basis as a room-temperature electrolyte sensor. In this work, a relatively stable perovskite room-temperature gas-sensitive material (indium acetate-functionalized perovskite CsPbBr 3 nanocrystals (NCs)) , was prepared by in situ solution synthesis method.…”