ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
Discovery of Lead-Free Perovskites for High-Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 74 citations in OpenAlex.
- Machine Learning-Driven Advances in Perovskite Materials and Solar Cells.Nanomaterials (Basel, Switzerland) · 2026Review
- Octahedral-motif-guided design of optoelectronic semiconductors via interpretable machine learning.Nature communications · 2026Article
- Decoupling Intrinsic Molecular Efficacy From Platform Effects: An Interpretable Machine Learning Framework for Unbiased Perovskite Passivator Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning for Designing Perovskites and Perovskite-Inspired Solar Materials: Emerging Opportunities and Challenges.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Algorithm-Guided Experimentation for Optimization of High-Performance Perovskite Solar Cells.ACS energy letters · 2025Article
- Tuning the properties of ASnTeRSC advances · 2025Article
- Studying the Thermodynamic Phase Stability of Organic-Inorganic Hybrid Perovskites Using Machine Learning.Molecules (Basel, Switzerland) · 2024Article
- Feature Selection in Machine Learning for Perovskite Materials Design and Discovery.Materials (Basel, Switzerland) · 2023Review
- Discovery of Lead-Free Perovskites for High-Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
Exploring lead-free candidates and improving efficiency and stability remain the obstacle of hybrid organic-inorganic perovskite-based devices commercialization. Traditional trial-and-error methods seriously restrict the discovery especially for large search space, complex crystal structure and multi-objective properties. Here, the authors propose a multi-step and multi-stage screening scheme to accelerate the discovery of hybrid organic-inorganic perovskites A
Indexed as
Identifiers
What OpenQuestion holds
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.