NestedAE: interpretable nested autoencoders for multi-scale materials characterization-Reference-Cited by-同舟云学术

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NestedAE: interpretable nested autoencoders for multi-scale materials characterization

Published:2024 Issue: Volume: Page:
ISSN:2051-6347
Container-title:Materials Horizons
language:en
Short-container-title:Mater. Horiz.

Author:

Thota Nikhil¹^ORCID,Priyadarshini Maitreyee Sharma²¹^ORCID,Hernandez Rigoberto²¹³^ORCID

Affiliation:

1. Chemical and Biomolecular Engineering Department, Johns Hopkins University, Baltimore, MD, USA

2. Chemistry Department, Johns Hopkins University, Baltimore, MD, USA

3. Materials Science and Engineering Department, Johns Hopkins University, Baltimore, MD, USA

Abstract

The multi-scale features and latent space are connected by a nested autoencoder.

Funder

Basic Energy Sciences

Division of Information and Intelligent Systems

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrical and Electronic Engineering,Process Chemistry and Technology,Mechanics of Materials,General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2024/MH/D3MH01484C

Reference36 articles.

1. I. R. E. Agency, World Energy Transitions Outlook 2023: 1.5 Pathway, 2023, https://www.irena.org/Publications/2023/Jun/World-Energy-Transitions-Outlook-2023 , Accessed on September 13, 2023

2. Multiscale Modeling Meets Machine Learning: What Can We Learn?

3. The Middle Science: Traversing Scale In Complex Many-Body Systems

4. Mesoscopic and multiscale modelling in materials

5. Multiscale materials modeling

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