相关论文: Machine learning regression analyses of intensity …
Understanding and controlling charge carrier recombination dynamics is essential for enhancing the performance of metal halide perovskite optoelectronic devices. In this study, we present a machine learning-assisted intensity-modulated…
Metal halide perovskites exhibit coupled electronic and ionic properties that determine their photovoltaic performance and operational stability. Understanding and quantifying ionic transport are therefore essential for advancing perovskite…
Halide perovskites exhibit unpredictable properties in response to environmental stressors, due to several composition-dependent degradation mechanisms. In this work, we apply data visualization and machine learning (ML) techniques to…
Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift-diffusion (DD) modelling is a reliable method, its mathematical complexity makes…
Potentiostatic impedance spectroscopy (IS) is a well stablished characterization technique for elucidating the electric resistivity and capacitive features of materials and devices. In the case of solar cells, by applying a small voltage…
Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost,…
In perovskite solar cells, photovoltaic action is created by charge transport layers (CTLs) either side of the light-absorbing metal halide perovskite semiconductor. Hence, the rates for desirable charge extraction and unwanted interfacial…
Metal halide perovskite (MHP) optoelectronics may become a viable alternative to standard Si-based technologies, but the current lack of long-term stability precludes their commercial adoption. Exposure to standard operational stressors…
The internal luminescence quantum efficiency ($Q_\mathrm{i}^\mathrm{lum}$) provides an excellent assessment of the optoelectronic quality of semiconductors. To determine $Q_\mathrm{i}^\mathrm{lum}$ of perovskite films from the…
We present a novel analytical model for analysing the spectral photoluminescence quantum yield of non-planar semiconductor thin films. This model considers the escape probability of luminescence and is applied to triple-cation perovskite…
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending…
The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at…
We demonstrate a machine learning (ML) approach that accurately predicts the current-voltage behavior of 3D/2D-structured (FAMA)Pb(IBr)3/OABr hybrid organic-inorganic halide perovskite (HOIP) solar cells under AM1.5 illumination. Our neural…
Owing to their exceptional semiconducting properties, hybrid inorganic-organic perovskites show great promise as photovoltaic absorbers. In these materials, long-range diffusion of charge carriers allows for most of the photogenerated…
Metal-halide perovskites (MHPs) have exciting optoelectronic properties and are under investigation for various applications, such as photovoltaics, light-emitting diodes, and lasers. An essential step towards exploiting the full potential…
Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning. In the application of high-throughput thickness as a downstream process of the high-throughput optimization of…
Machine learning (ML) has been extensively employed in planar perovskite photovoltaics to screen effective organic molecular additives, while encountering predictive biases for novel materials due to small datasets and reliance on…
Defects in metal halide perovskites (MHP) are photosensitive, making the observer effect unavoidable when laser spectroscopy methods are applied. Photoluminescence (PL) bleaching and enhancement under light soaking and recovery in dark are…
Scanning Electron Microscopy (SEM) is indispensable for characterizing the microstructure of thin films during perovskite solar cell fabrication. Accurate identification and quantification of lead iodide and perovskite phases are critical…
Cesium based halide perovskites, such as CsPbI3 and CsSnI3, have emerged as exceptional candidates for next generation photovoltaic and optoelectronic technologies, but their practical application is limited by temperature dependent phase…