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Title: Extrapolative Bayesian optimization with Gaussian process and neural network ensemble surrogate models
Authors: Lim, Yee-Fun
Ng, Chee Koon
Vaitesswar, U. S.
Hippalgaonkar, Kedar
Keywords: Engineering::Materials
Issue Date: 2021
Source: Lim, Y., Ng, C. K., Vaitesswar, U. S. & Hippalgaonkar, K. (2021). Extrapolative Bayesian optimization with Gaussian process and neural network ensemble surrogate models. Advanced Intelligent Systems, 3(11), 2100101-.
Project: A1898b0043 
Journal: Advanced Intelligent Systems 
Abstract: 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.
ISSN: 2640-4567
DOI: 10.1002/aisy.202100101
Schools: School of Materials Science and Engineering 
Organisations: Institute of Materials Research and Engineering, A*STAR 
Rights: © 2021 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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