NestedAE: interpretable nested autoencoders for multi-scale materials characterization

Nikhil Thota; Maitreyee Sharma Priyadarshini; Rigoberto Hernandez

doi:10.1039/d3mh01484c

NestedAE: interpretable nested autoencoders for multi-scale materials characterization

Mater Horiz. 2024 Feb 6;11(3):700-707. doi: 10.1039/d3mh01484c.

Authors

Nikhil Thota¹, Maitreyee Sharma Priyadarshini^{2

1}, Rigoberto Hernandez^{2

1

3}

Affiliations

¹ Chemical and Biomolecular Engineering Department, Johns Hopkins University, Baltimore, MD, USA.
² Chemistry Department, Johns Hopkins University, Baltimore, MD, USA. hernandez@jhu.edu.
³ Materials Science and Engineering Department, Johns Hopkins University, Baltimore, MD, USA.

PMID: 37991466
DOI: 10.1039/d3mh01484c

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 (J-V) 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.