Application of Machine Learning in Perovskite Solar Cell Crystal Size Distribution Analysis
- PDF / 441,677 Bytes
- 8 Pages / 432 x 648 pts Page_size
- 90 Downloads / 162 Views
MRS Advances © 2019 Materials Research Society DOI: 10.1557/adv.2019.145
Application of Machine Learning in Perovskite Solar Cell Crystal Size Distribution Analysis Thomas Chen1, Yuchen Zhou2, Miriam Rafailovich3 1
Mission San Jose High School, 2Stonybrook University, 3State University at Stony brook
Abstract This research automates edge detection for perovskite crystal grains using machine learning (ML). Once the edges of the crystal grains are located, a flood-fill algorithm can be used to find the distribution of crystal grain areas. The ML algorithm uses GNU Octave to run a regularized logistic regression algorithm that classifies each pixel of an input image as part of an edge or not part of an edge. The basic features used for the algorithm are each pixel’s grayscale intensity, its Sobel derivative. Higher order Sobel derivatives, higher degree polynomial terms, and intensities convolved by various kernels were used as additional features to improve the program’s accuracy and true-positive rate. Training data is obtained by using non-ML Canny Edge Detection to annotate the edges an SEM image of a pure perovskite solar cell (PSC). The classifier exhibits an 85.58% accuracy and produces an edge mask that clearly outlines the crystals visually. The ML edge mask exhibits far fewer falsepositive mis-classifications for pixels in the middle of the crystals than Canny. However, the ML mask’s edges are fainter, owing to a lower true-positive classification rate. Using more kernels, higher order derivatives, and higher degree polynomial terms all significantly increased the true positive rate of the classifier, leading to thicker edges. This algorithm can greatly accelerate perovskite solar cell research (and potentially any research requiring particle size analysis), automating a process scientists previously had to perform by hand. This will facilitate the search for a solution for the world’s growing demands for renewable energy.
INTRODUCTION: In recent years, there has been a sixfold increase in the power conversion efficiency (PCE) of perovskite solar cells (PSCs)[5]. PSCs have many advantages over established photovoltaic (PV) technologies, such as its low-cost, rapid manufacturing process and its exceptional optoelectronic properties: absorption, photoluminescence, and low charge-recombination rates[5, 10].
793
Downloaded from https://www.cambridge.org/core. Linkoping University Library, on 06 Jan 2020 at 22:27:37, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1557/adv.2019.145
Perovskites are hybrid organic-inorganic metal halides with a structural formula ABX3, where A is an organic cation, B is an inorganic cation, and X is a halide anion[5]. Shown in figure 1, the ideal unit cell of the perovskite is cubic, with A cation in the center, B cations in the 8 corners of the cube, and X anions arranged octahedrally around the B’s in the corners[5]. Of the four main designs of the perovskite solar cell, the standard planar design is used h
Data Loading...
