ArticleACS omega2022
Inverse Design of Hybrid Organic-Inorganic Perovskites with Suitable Bandgaps via Proactive Searching Progress.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Integrating NSGA-II and TOPSIS for Stacking Model Optimization in Pursuit of Halide Double Perovskite Screening.Materials (Basel, Switzerland) · 2026Article
- Density Estimation Based on Mixtures of Gaussians for Perovskite Solar Cells Modeling.Journal of chemical information and modeling · 2026Article
- Inverse Design of Low-Resistivity Ternary Gold Alloys via Interpretable Machine Learning and Proactive Search Progress.Materials (Basel, Switzerland) · 2024Article
- Feature Selection in Machine Learning for Perovskite Materials Design and Discovery.Materials (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hybrid organic-inorganic perovskites (HOIPs) have shown the encouraging development in solar cells that have achieved excellent device performance. One of the most important issues has been focused on finding Pb-free candidates with suitable bandgaps, which could accelerate the commercialization of environmentally friendly HOIP-based cells. Herein, we propose a new inverse design method, proactive searching progress (PSP), to efficiently discover potential HOIPs from universal chemical space by combining machine learning (ML) techniques. Compared to the pioneering work on this topic, we carried out our ML study based on 1201 collected HOIP samples with experimental bandgaps rather than theoretical properties. On the basis of 25 selected features, a weighted voting regressor ML model was constructed to predict bandgaps of HOIPs. The model comprehensively embedded four submodels and performed the coefficient determinations of 0.95 for leaving-one-out cross-validation and 0.91 for testing set. The feature analysis revealed that the tolerance factor (
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Registered trials
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