Predicting Organic-Inorganic Halide Perovskite Photovoltaic Performance from Optical Properties of Constituent Films through Machine Learning

• URL: http://arxiv.org/abs/2412.09638v1
• Date: Fri, 06 Dec 2024 18:41:21 GMT
• Title: Predicting Organic-Inorganic Halide Perovskite Photovoltaic Performance from Optical Properties of Constituent Films through Machine Learning
• Authors: Ruiqi Zhang, Brandon Motes, Shaun Tan, Yongli Lu, Meng-Chen Shih, Yilun Hao, Karen Yang, Shreyas Srinivasan, Moungi G. Bawendi, Vladimir Bulovic,
• Abstract summary: We demonstrate a machine learning 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 network algorithm is trained on measured responses from several hundred HOIP solar cells.
• Score: 3.932295988013435
• License:
• Abstract: 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 network algorithm is trained on measured responses from several hundred HOIP solar cells, using three simple optical measurements of constituent HOIP films as input: optical transmission spectrum, spectrally-resolved photoluminescence, and time-resolved photoluminescence, from which we predict the open-circuit voltage (Voc), short-circuit current (Jsc), and fill factors (FF) values of solar cells that contain the HOIP active layers. Determined average prediction accuracies for 95 % of the predicted Voc, Jsc, and FF values are 91%, 94% and 89%, respectively, with R2 coefficients of determination of 0.47, 0.77, and 0.58, respectively. Quantifying the connection between ML predictions and physical parameters extracted from the measured HOIP films optical properties, allows us to identify the most significant parameters influencing the prediction results. With separate ML-classifying algorithms, we identify degraded solar cells using the same optical input data, achieving over 90% classification accuracy through support vector machine, cross entropy loss, and artificial neural network algorithms. To our knowledge, the demonstrated regression and classification work is the first to use ML to predict device photovoltaic properties solely from the optical properties of constituent materials.

Related papers

• Sample Classification using Machine Learning-Assisted Entangled Two-Photon Absorption [0.0]
  Entangled two-photon absorption (eTPA) has been recognized as a potentially powerful tool for ultra-sensitive spectroscopy. We introduce an experimental scheme that reduces the amount of data needed to identify and classify unknown samples.
  arXiv  Detail & Related papers  (2025-01-30T18:00:15Z)
• Site-Controlled Purcell-Induced Bright Single Photon Emitters in Hexagonal Boron Nitride [62.170141783047974]
  Single photon emitters hosted in hexagonal boron nitride (hBN) are essential building blocks for quantum photonic technologies that operate at room temperature. We experimentally demonstrate large-area arrays of plasmonic nanoresonators for Purcell-induced site-controlled SPEs. Our results offer arrays of bright, heterogeneously integrated quantum light sources, paving the way for robust and scalable quantum information systems.
  arXiv  Detail & Related papers  (2024-05-03T23:02:30Z)
• Spatial super-resolution in nanosensing with blinking emitters [79.16635054977068]
  We propose a method of spatial resolution enhancement in metrology with blinking fluorescent nanosensors. We believe that blinking fluorescent sensing agents being complemented with the developed image analysis technique could be utilized routinely in the life science sector.
  arXiv  Detail & Related papers  (2024-02-27T10:38:05Z)
• Comparative Study of MPPT and Parameter Estimation of PV cells [0.0]
  The presented work focuses on utilising machine learning techniques to accurately estimate accurate values for known and unknown parameters of the PVLIB model for solar cells and photovoltaic modules. An Artificial Neural Network (ANN) algorithm was employed, which outperformed other metaheuristic and machine learning algorithms in terms of computational efficiency.
  arXiv  Detail & Related papers  (2023-04-16T15:45:28Z)
• Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach [0.0]
  Phosphorescent metal complexes have been under intense investigations as emissive dopants for energy efficient organic light emitting diodes (OLEDs) To render their practical applications as OLED emitters, it is in great need to develop Pt(II) complexes with high radiative decay rate constant ($k_r$) and photoluminescence (PL) quantum yield. Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters.
  arXiv  Detail & Related papers  (2023-01-08T04:54:33Z)
• Photonic Quantum Computing For Polymer Classification [62.997667081978825]
  Two polymer classes visual (VIS) and near-infrared (NIR) are defined based on the size of the polymer gaps. We present a hybrid classical-quantum approach to the binary classification of polymer structures.
  arXiv  Detail & Related papers  (2022-11-22T11:59:52Z)
• Bipolar single-molecule electroluminescence and electrofluorochromism [50.591267188664666]
  We investigate cationic and anionic fluorescence of individual zinc phthalocyanine (ZnPc) molecules adsorbed on ultrathin NaCl films on Ag (111) by using STML. They depend on the tip-sample bias polarity and appear at threshold voltages that are correlated with the onset energies of particular molecular orbitals.
  arXiv  Detail & Related papers  (2022-10-20T09:22:45Z)
• Transfer Learning for Rapid Extraction of Thickness from Optical Spectra of Semiconductor Thin Films [11.894117300649372]
  thicknessML rapidly extracts film thickness from spectroscopic reflection and transmission. We demonstrate that thicknessML can extract film thickness from six perovskite samples in a two-stage process. Results show a pre-training thickness mean absolute percentage error (MAPE) of 5-7% and an experimental thickness MAPE of 6-19%.
  arXiv  Detail & Related papers  (2022-06-14T16:26:15Z)
• Machine Learning-enhanced Efficient Spectroscopic Ellipsometry Modeling [2.502933334555377]
  We utilize Machine Learning to facilitate efficient film fabrication, specifically Atomic Layer Deposition (ALD) In this paper, we propose an ML-based approach to expedite film thickness estimation.
  arXiv  Detail & Related papers  (2022-01-01T19:53:03Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
  Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry. New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications. We present a novel technique to reconstruct the transfer matrix of linear optical networks.
  arXiv  Detail & Related papers  (2020-10-01T16:04:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.