Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models

Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of experimental parameters in automated active learning driven high throughput experiments in materials science and chemistry. Previous studies suggest that optimization performance of the typical surrogate m...

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Autores principales:	Yee-Fun Lim, Chee Koon Ng, U.S. Vaitesswar, Kedar Hippalgaonkar
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	automated experiments Bayesian optimization extrapolative algorithms machine learning neural network ensembles Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225
Acceso en línea:	https://doaj.org/article/7b396f6d97e843f0b445409a937ba3a1
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id	oai:doaj.org-article:7b396f6d97e843f0b445409a937ba3a1
record_format	dspace
spelling	oai:doaj.org-article:7b396f6d97e843f0b445409a937ba3a12021-11-23T07:58:48ZExtrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models2640-456710.1002/aisy.202100101https://doaj.org/article/7b396f6d97e843f0b445409a937ba3a12021-11-01T00:00:00Zhttps://doi.org/10.1002/aisy.202100101https://doaj.org/toc/2640-4567Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of experimental parameters in automated active learning driven high throughput experiments in materials science and chemistry. Previous studies suggest that optimization performance of the typical surrogate model in the BO algorithm, Gaussian processes (GPs), may be limited due to its inability to handle complex datasets. Herein, various surrogate models for BO, including GPs and neural network ensembles (NNEs), are investigated. Two materials datasets of different complexity with different properties are used, to compare the performance of GP and NNE—the first is the compressive strength of concrete (8 inputs and 1 target), and the second is a simulated high‐dimensional dataset of thermoelectric properties of inorganic materials (22 inputs and 1 target). While NNEs can converge faster toward optimum values, GPs with optimized kernels are able to ultimately achieve the best evaluated values after 100 iterations, even for the most complex dataset. This surprising result is contrary to expectations. It is believed that these findings shed new light on the understanding of surrogate models for BO, and can help accelerate the inverse design of new materials with better structural and functional performance.Yee-Fun LimChee Koon NgU.S. VaitesswarKedar HippalgaonkarWileyarticleautomated experimentsBayesian optimizationextrapolative algorithmsmachine learningneural network ensemblesComputer engineering. Computer hardwareTK7885-7895Control engineering systems. Automatic machinery (General)TJ212-225ENAdvanced Intelligent Systems, Vol 3, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	automated experiments Bayesian optimization extrapolative algorithms machine learning neural network ensembles Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225
spellingShingle	automated experiments Bayesian optimization extrapolative algorithms machine learning neural network ensembles Computer engineering. Computer hardware TK7885-7895 Control engineering systems. Automatic machinery (General) TJ212-225 Yee-Fun Lim Chee Koon Ng U.S. Vaitesswar Kedar Hippalgaonkar Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models
description	Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of experimental parameters in automated active learning driven high throughput experiments in materials science and chemistry. Previous studies suggest that optimization performance of the typical surrogate model in the BO algorithm, Gaussian processes (GPs), may be limited due to its inability to handle complex datasets. Herein, various surrogate models for BO, including GPs and neural network ensembles (NNEs), are investigated. Two materials datasets of different complexity with different properties are used, to compare the performance of GP and NNE—the first is the compressive strength of concrete (8 inputs and 1 target), and the second is a simulated high‐dimensional dataset of thermoelectric properties of inorganic materials (22 inputs and 1 target). While NNEs can converge faster toward optimum values, GPs with optimized kernels are able to ultimately achieve the best evaluated values after 100 iterations, even for the most complex dataset. This surprising result is contrary to expectations. It is believed that these findings shed new light on the understanding of surrogate models for BO, and can help accelerate the inverse design of new materials with better structural and functional performance.
format	article
author	Yee-Fun Lim Chee Koon Ng U.S. Vaitesswar Kedar Hippalgaonkar
author_facet	Yee-Fun Lim Chee Koon Ng U.S. Vaitesswar Kedar Hippalgaonkar
author_sort	Yee-Fun Lim
title	Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models
title_short	Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models
title_full	Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models
title_fullStr	Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models
title_full_unstemmed	Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models
title_sort	extrapolative bayesian optimization with gaussian process and neural network ensemble surrogate models
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/7b396f6d97e843f0b445409a937ba3a1
work_keys_str_mv	AT yeefunlim extrapolativebayesianoptimizationwithgaussianprocessandneuralnetworkensemblesurrogatemodels AT cheekoonng extrapolativebayesianoptimizationwithgaussianprocessandneuralnetworkensemblesurrogatemodels AT usvaitesswar extrapolativebayesianoptimizationwithgaussianprocessandneuralnetworkensemblesurrogatemodels AT kedarhippalgaonkar extrapolativebayesianoptimizationwithgaussianprocessandneuralnetworkensemblesurrogatemodels
_version_	1718416836419125248

Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models

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