Issue 6, 2022

Bayesian optimization in continuous spaces via virtual process embeddings

Abstract

Automated chemical synthesis, materials fabrication, and spectroscopic physical measurements often bring forth the challenge of process trajectory optimization, i.e., discovering the time dependence of temperature, electric field, or pressure that gives rise to optimal properties. Due to the high dimensionality of the corresponding vectors, these problems are not directly amenable to Bayesian Optimization (BO). Here we propose an approach based on the combination of the generative statistical models, specifically variational autoencoders, and Bayesian optimization. Here, the set of potential trajectories is formed based on best practices in the field, domain intuition, or human expertise. The variational autoencoder is used to encode the thus generated trajectories as a latent vector, and also allows for the generation of trajectories via sampling from latent space. In this manner, Bayesian optimization of the process is realized in the latent space of the system, reducing the problem to a low-dimensional one. Here we apply this approach to a ferroelectric lattice model and demonstrate that this approach allows discovering the field trajectories that maximize curl in the system. The analysis of the corresponding polarization and curl distributions allows the relevant physical mechanisms to be decoded.

Graphical abstract: Bayesian optimization in continuous spaces via virtual process embeddings

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Article information

Article type
Paper
Submitted
22 Jun 2022
Accepted
14 Okt 2022
First published
04 Nov 2022
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2022,1, 910-925

Bayesian optimization in continuous spaces via virtual process embeddings

M. Valleti, R. K. Vasudevan, M. A. Ziatdinov and S. V. Kalinin, Digital Discovery, 2022, 1, 910 DOI: 10.1039/D2DD00065B

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