Improving the Interpretability of ANN-Based Predictions of Lattice Constants in Aliovalently Doped Perovskites Using Partial Dependence Plots

Improving the Interpretability of ANN-Based Predictions of Lattice Constants in Aliovalently Doped Perovskites Using Partial Dependence Plots

The relationship between structure and properties is fundamental in materials science, particularly for aliovalently doped perovskites, where structural changes significantly influence material performance. Accurate prediction of key structural parameters is essential for tailoring these materials f...

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Bibliographic Details
Main Authors: Abdullah Alharthi, Abdulgafor Alfares, Yusuf Abubakar Sha’aban, Dahood Ademuyiwa Adegbite
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Crystals
Subjects:
perovskite
aliovalent doping
artificial neural networks (ANNs)
lattice constants
machine learning interpretability
partial dependence plots (PDPs)
Online Access:https://www.mdpi.com/2073-4352/15/6/538
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