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Artificial Intelligence in Materials Modeling and Design

J. S. Huang, K. M. Liew, Adesola Ademiloye Orcid Logo

Archives of Computational Methods in Engineering, Volume: 28, Issue: 5, Pages: 3399 - 3413

Swansea University Author: Adesola Ademiloye Orcid Logo

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DOI (Published version): 10.1007/s11831-020-09506-1

Abstract

In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper,...

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Published in: Archives of Computational Methods in Engineering
ISSN: 1134-3060 1886-1784
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa55399
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last_indexed 2021-09-08T03:18:09Z
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spelling 2021-09-07T15:18:13.9255745 v2 55399 2020-10-12 Artificial Intelligence in Materials Modeling and Design e37960ed89a7e3eaeba2201762626594 0000-0002-9741-6488 Adesola Ademiloye Adesola Ademiloye true false 2020-10-12 EAAS In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented. Journal Article Archives of Computational Methods in Engineering 28 5 3399 3413 Springer Science and Business Media LLC 1134-3060 1886-1784 1 8 2021 2021-08-01 10.1007/s11831-020-09506-1 http://dx.doi.org/10.1007/s11831-020-09506-1 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University 2021-09-07T15:18:13.9255745 2020-10-12T12:19:44.2507921 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering J. S. Huang 1 K. M. Liew 2 Adesola Ademiloye 0000-0002-9741-6488 3 55399__18407__d37d7b9ce4bd4afcb7f8fe6283255003.pdf 55399.pdf 2020-10-12T12:23:11.7282199 Output 1474473 application/pdf Accepted Manuscript true 2021-10-11T00:00:00.0000000 true eng
title Artificial Intelligence in Materials Modeling and Design
spellingShingle Artificial Intelligence in Materials Modeling and Design
Adesola Ademiloye
title_short Artificial Intelligence in Materials Modeling and Design
title_full Artificial Intelligence in Materials Modeling and Design
title_fullStr Artificial Intelligence in Materials Modeling and Design
title_full_unstemmed Artificial Intelligence in Materials Modeling and Design
title_sort Artificial Intelligence in Materials Modeling and Design
author_id_str_mv e37960ed89a7e3eaeba2201762626594
author_id_fullname_str_mv e37960ed89a7e3eaeba2201762626594_***_Adesola Ademiloye
author Adesola Ademiloye
author2 J. S. Huang
K. M. Liew
Adesola Ademiloye
format Journal article
container_title Archives of Computational Methods in Engineering
container_volume 28
container_issue 5
container_start_page 3399
publishDate 2021
institution Swansea University
issn 1134-3060
1886-1784
doi_str_mv 10.1007/s11831-020-09506-1
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
url http://dx.doi.org/10.1007/s11831-020-09506-1
document_store_str 1
active_str 0
description In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented.
published_date 2021-08-01T14:05:12Z
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score 11.247077

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