Bayesian optimization in continuous spaces <i>via</i> virtual process embeddings-Reference-Cited by-同舟云学术

Bayesian optimization in continuous spaces via virtual process embeddings

Published:2022 Issue:6 Volume:1 Page:910-925
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Valleti Mani¹^ORCID,Vasudevan Rama K.²,Ziatdinov Maxim A.²³,Kalinin Sergei V.⁴

Affiliation:

1. Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, TN 37916, USA

2. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA

3. Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA

4. Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37916, USA

Abstract

Process optimization in the latent space of functions via variational autoencoder (VAE) and Bayesian Optimization (BO). We demonstrate this to optimize the curl of a kinetic ferroelectric model.

Funder

U.S. Department of Energy

Basic Energy Sciences

Energy Frontier Research Centers

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/DD/D2DD00065B

Reference52 articles.

1. Self-driving laboratory for accelerated discovery of thin-film materials

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