ReviewMaterials (Basel, Switzerland)2023
Feature Selection in Machine Learning for Perovskite Materials Design and Discovery.
Review in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Integrating NSGA-II and TOPSIS for Stacking Model Optimization in Pursuit of Halide Double Perovskite Screening.Materials (Basel, Switzerland) · 2026Article
- Rational Design of Single-Phase High-Entropy Oxides via Large Language Model Data Mining and Explainable Machine Learning.Journal of chemical information and modeling · 2026Article
- Machine Learning for Designing Perovskites and Perovskite-Inspired Solar Materials: Emerging Opportunities and Challenges.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Artificial Intelligence for Perovskite Additive Engineering: From Molecular Screening to Autonomous Discovery.Molecules (Basel, Switzerland) · 2026Review
- BEmXRD-Nets framework for novel machine learning models to predict crystal energy with diversity structures.Scientific reports · 2026Article
- Multi-Scale Feature Attention Network for Rapid and Non-Destructive Quantification of Aflatoxin BFoods (Basel, Switzerland) · 2025Article
- Interpretable Prediction and Analysis of PVA Hydrogel Mechanical Behavior Using Machine Learning.Gels (Basel, Switzerland) · 2025Article
- Investigating long-term risk of aortic aneurysm and dissection from fluoroquinolones and the key contributing factors using machine learning methods.Scientific reports · 2025Article
- Machine learning-enhanced band gaps prediction for low-symmetry double and layered perovskites.Scientific reports · 2024Article
- Plasmonics Meets Perovskite Photovoltaics: Innovations and Challenges in Boosting Efficiency.Molecules (Basel, Switzerland) · 2024Review
- Predicting Yield Strength and Plastic Elongation in Body-Centered Cubic High-Entropy Alloys.Materials (Basel, Switzerland) · 2024Article
- Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization.Nature communications · 2024Article
- A data-driven QSPR model for screening organic corrosion inhibitors for carbon steel using machine learning techniques.RSC advances · 2024Article
- Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures.Materials (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.