ArticleThe journal of physical chemistry letters2025
Tracing Ion Migration in Halide Perovskites with Machine Learned Force Fields.
Article in The journal of physical chemistry letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Article
- Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces.ACS applied materials & interfaces · 2026Review
- Ion migration in perovskite solar cells.Nature reviews. Chemistry · 2026Review
- Artificial Intelligence for Perovskite Additive Engineering: From Molecular Screening to Autonomous Discovery.Molecules (Basel, Switzerland) · 2026Review
- Ionic Losses and Gains in Perovskite Solar Cells: Impact on Efficiency and Stability.ACS energy letters · 2025Article
- Determining the Ion Mobility in Perovskite Solar Cells from Impedance Spectroscopy.ACS energy letters · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Halide perovskite optoelectronic devices suffer from chemical degradation and current-voltage hysteresis induced by migration of highly mobile charged defects. Atomic scale molecular dynamics simulations can capture the motion of these ionic defects, but classical force fields are too inflexible to describe their dynamical charge states. Using CsPbI
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Registered trials
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