Compositional and elemental descriptors for perovskite materialsDownload PDF

Published: 17 Mar 2023, Last Modified: 21 Apr 2023ml4materials-iclr2023 PosterReaders: Everyone
Keywords: perovskites, experimental dataset, machine learning, PCA, WAE, regression
TL;DR: For experimental bandgap regression task, the elemental descriptors were superior to weight composition descriptors that were in turn superior to molar composition descriptors.
Abstract: In this extended abstract we compare the performance of different families of descriptors – \textit{molar composition descriptor, weight composition descriptor and elemental descriptor} – for regression task (prediction of bandgap) and include examples of a classification task for perovskite oxide materials with general formulas $ABO_3$, $A_2BB’O_6$, and $A_xA’_{1-x}B_yB’_{1-y}O_6$. The best performance was observed for our elemental descriptor which consisted of $A$-site and $B$-site element information on: Shannon’s ionic radius, ideal bond length, electronegativity, van der Waals radius, ionization energy, molar volume, atomic number, and atomic mass. The weight composition descriptor showed superior results over a simpler molar composition descriptor. The results of principal component analysis, regression models with the hyperparameters optimized using an autoML software and Wasserstein autoencoders are briefly discussed for a possible use in inverse materials design.
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