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 စက် 2023
Accepted
15 နို 2023
First published
15 နို 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

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements