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Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning
In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A genera...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055566/ https://www.ncbi.nlm.nih.gov/pubmed/32154072 http://dx.doi.org/10.1002/advs.201902607 |
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author | Chen, Chun‐Teh Gu, Grace X. |
author_facet | Chen, Chun‐Teh Gu, Grace X. |
author_sort | Chen, Chun‐Teh |
collection | PubMed |
description | In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A general‐purpose inverse design approach is presented using generative inverse design networks. This ML‐based inverse design approach uses backpropagation to calculate the analytical gradients of an objective function with respect to design variables. This inverse design approach is capable of overcoming local minima traps by using backpropagation to provide rapid calculations of gradient information and running millions of optimizations with different initial values. Furthermore, an active learning strategy is adopted in the inverse design approach to improve the performance of candidate materials and reduce the amount of training data needed to do so. Compared to passive learning, the active learning strategy is capable of generating better designs and reducing the amount of training data by at least an order‐of‐magnitude in the case study on composite materials. The inverse design approach is compared with conventional gradient‐based topology optimization and gradient‐free genetic algorithms and the pros and cons of each method are discussed when applied to materials discovery and design problems. |
format | Online Article Text |
id | pubmed-7055566 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70555662020-03-09 Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning Chen, Chun‐Teh Gu, Grace X. Adv Sci (Weinh) Full Papers In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A general‐purpose inverse design approach is presented using generative inverse design networks. This ML‐based inverse design approach uses backpropagation to calculate the analytical gradients of an objective function with respect to design variables. This inverse design approach is capable of overcoming local minima traps by using backpropagation to provide rapid calculations of gradient information and running millions of optimizations with different initial values. Furthermore, an active learning strategy is adopted in the inverse design approach to improve the performance of candidate materials and reduce the amount of training data needed to do so. Compared to passive learning, the active learning strategy is capable of generating better designs and reducing the amount of training data by at least an order‐of‐magnitude in the case study on composite materials. The inverse design approach is compared with conventional gradient‐based topology optimization and gradient‐free genetic algorithms and the pros and cons of each method are discussed when applied to materials discovery and design problems. John Wiley and Sons Inc. 2020-01-09 /pmc/articles/PMC7055566/ /pubmed/32154072 http://dx.doi.org/10.1002/advs.201902607 Text en © 2020 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Full Papers Chen, Chun‐Teh Gu, Grace X. Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning |
title | Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning |
title_full | Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning |
title_fullStr | Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning |
title_full_unstemmed | Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning |
title_short | Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning |
title_sort | generative deep neural networks for inverse materials design using backpropagation and active learning |
topic | Full Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055566/ https://www.ncbi.nlm.nih.gov/pubmed/32154072 http://dx.doi.org/10.1002/advs.201902607 |
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