Issue 3, 2024

NestedAE: interpretable nested autoencoders for multi-scale materials characterization

Abstract

We introduce an interpretable machine learning architecture, NestedAE, for multiscale materials using nested supervised autoencoders. We benchmarked the performance of NestedAE on two databases: (1) a synthetic dataset created from nested analytical functions whose dimensionality is therefore known a priori, and (2) a multiscale MHP dataset that is a combination of an open source dataset containing atomic and ionic properties, and a second dataset containing device characterization using current density–voltage (JV) analysis. The NestedAE architecture was found to have higher noise robustness and lower reconstruction losses when compared to a vanilla autoencoder (AE). Its application on the MHP dataset revealed links between crystal scale properties and device performance in agreement with earlier experimental observations.

Graphical abstract: NestedAE: interpretable nested autoencoders for multi-scale materials characterization

Supplementary files

Article information

Article type
Communication
Submitted
16 sep. 2023
Accepted
15 nov. 2023
First published
15 nov. 2023

Mater. Horiz., 2024,11, 700-707

NestedAE: interpretable nested autoencoders for multi-scale materials characterization

N. Thota, M. S. Priyadarshini and R. Hernandez, Mater. Horiz., 2024, 11, 700 DOI: 10.1039/D3MH01484C

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