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Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics

https://doi.org/10.1002/adts.201900178
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Chemical Origin of the Stability Difference between Copper(I)‐ and Silver(I)‐Based Halide Double Perovskites
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Methylammonium Rotational Dynamics in Lead Halide Perovskite by Classical Molecular Dynamics: The Role of Temperature
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Extending the Compositional Space of Mixed Lead Halide Perovskites by Cs, Rb, K, and Na Doping
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Mixed Sn–Ge Perovskite for Enhanced Perovskite Solar Cell Performance in Air
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Semiconducting Tin and Lead Iodide Perovskites with Organic Cations: Phase Transitions, High Mobilities, and Near-Infrared Photoluminescent Properties
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All-Inorganic Perovskite Solar Cells
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Lead-Free Mixed Tin and Germanium Perovskites for Photovoltaic Application
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CsPb0.9Sn0.1IBr2 Based All-Inorganic Perovskite Solar Cells with Exceptional Efficiency and Stability
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Anomalous Alloy Properties in Mixed Halide Perovskites
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The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
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Machine learning bandgaps of double perovskites
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Structures, Phase Transitions and Tricritical Behavior of the Hybrid Perovskite Methyl Ammonium Lead Iodide
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Machine-learned approximations to Density Functional Theory Hamiltonians
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Formamidinium lead trihalide: a broadly tunable perovskite for efficient planar heterojunction solar cells
inferredcites →https://doi.org/10.1039/c6cp02105kopenalex-snapshot
Mixed Ge/Pb perovskite light absorbers with an ascendant efficiency explored from theoretical view
inferredcites →https://doi.org/10.1039/c6ee00030dopenalex-snapshot
Exploration of the compositional space for mixed lead halogen perovskites for high efficiency solar cells
inferredcites →https://doi.org/10.1039/c7ta09122bopenalex-snapshot
Mixed halide hybrid perovskites: a paradigm shift in photovoltaics
inferredcites →https://doi.org/10.1080/14686996.2018.1460176openalex-snapshot
Perovskite solar cells: must lead be replaced – and can it be done?
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How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
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Methylammonium fragmentation in amines as source of localized trap levels and the healing role of Cl in hybrid lead-iodide perovskites
inferredcites →https://doi.org/10.1103/physrevmaterials.2.025401openalex-snapshot
Octahedral tilting instabilities in inorganic halide perovskites
inferredcites →https://doi.org/10.1103/physrevmaterials.2.043802openalex-snapshot
Predictions of new AB O 3 perovskite compounds by combining machine learning and density functional theory
inferredcites →https://doi.org/10.1103/physrevmaterials.2.055401openalex-snapshot
Double perovskites overtaking the single perovskites: A set of new solar harvesting materials with much higher stability and efficiency
inferredcites →https://doi.org/10.1126/science.aad5845openalex-snapshot
A mixed-cation lead mixed-halide perovskite absorber for tandem solar cells
inferredcites →https://doi.org/10.1126/science.aah5557openalex-snapshot
Incorporation of rubidium cations into perovskite solar cells improves photovoltaic performance
inferredcites →https://doi.org/10.1134/s1063774514050198openalex-snapshot
Combined atomic ordering in the A and B sublattices of perovskite structure

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Accelerated Multi‐Property Screening of Lead‐Free Halide Double Perovskite via Transfer Learning
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High‐Accuracy Machine Learning Projections of Composition‐Dependent Thermal Stability in Halide Perovskites
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Physics‐Aware Hybrid Equivariant Graph Neural Networks for Robust Design of Inorganic Perovskite Photodetector
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Machine‐Learning‐Assisted Discovery of Mechanosynthesized Lead‐Free Metal Halide Perovskites for the Oxidative Photocatalytic Cleavage of Alkenes
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