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Advances in materials informatics: A review

Materials informatics (MI) is aimed to accelerate the materials discovery using computational intelligence and data science. Progress of MI depends on the strength of database and artificial intelligence protocols comprising machine learning (ML) and deep learning (DL) frameworks. Conventional ML mo...

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Main Authors: Sivan, Dawn, Kumar, K. Satheesh, Aziman, Abdullah, Raj, Veena, Izan Izwan, Misnon, Ramakrishna, Seeram, Jose, Rajan
Format: Article
Language:English
English
Published: Springer 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/40459/1/Advances%20in%20materials%20informatics.pdf
http://umpir.ump.edu.my/id/eprint/40459/2/Advances%20in%20materials%20informatics_FULL.pdf
http://umpir.ump.edu.my/id/eprint/40459/
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spelling my.ump.umpir.404592024-03-04T00:29:34Z http://umpir.ump.edu.my/id/eprint/40459/ Advances in materials informatics: A review Sivan, Dawn Kumar, K. Satheesh Aziman, Abdullah Raj, Veena Izan Izwan, Misnon Ramakrishna, Seeram Jose, Rajan QA75 Electronic computers. Computer science Materials informatics (MI) is aimed to accelerate the materials discovery using computational intelligence and data science. Progress of MI depends on the strength of database and artificial intelligence protocols comprising machine learning (ML) and deep learning (DL) frameworks. Conventional ML models are simple and interpretable, relying on statistical techniques and algorithms to learn patterns and make predictions with limited data. Conversely, DL, an advancement of ML, employs mathematical neural networks to automatically extract features and handle intricate data at the cost of data size and computational complexity. This work aims to provide a state-of-the-art understanding of the tools, data sources and techniques used in MI and their benefits and challenges. We evaluate the growth of MI through its subfields and track the main path of its advancement for artificial intelligence-driven materials discovery. The advancements in computational intelligence via machine learning and deep learning algorithms in different fields of materials science are discussed. As a specific example, understanding of materials properties using microstructural images is reviewed. Future demands and research prospects in materials science utilizing materials informatics have also been comprehensively analyzed. Springer 2024-02 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40459/1/Advances%20in%20materials%20informatics.pdf pdf en http://umpir.ump.edu.my/id/eprint/40459/2/Advances%20in%20materials%20informatics_FULL.pdf Sivan, Dawn and Kumar, K. Satheesh and Aziman, Abdullah and Raj, Veena and Izan Izwan, Misnon and Ramakrishna, Seeram and Jose, Rajan (2024) Advances in materials informatics: A review. Journal of Materials Science, 59. pp. 2602-2643. ISSN 0022-2461 (Print); 1573-4803 (Online). (Published) htps://doi.org/10.1007/s10853-024-09379-w htps://doi.org/10.1007/s10853-024-09379-w
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Sivan, Dawn
Kumar, K. Satheesh
Aziman, Abdullah
Raj, Veena
Izan Izwan, Misnon
Ramakrishna, Seeram
Jose, Rajan
Advances in materials informatics: A review
description Materials informatics (MI) is aimed to accelerate the materials discovery using computational intelligence and data science. Progress of MI depends on the strength of database and artificial intelligence protocols comprising machine learning (ML) and deep learning (DL) frameworks. Conventional ML models are simple and interpretable, relying on statistical techniques and algorithms to learn patterns and make predictions with limited data. Conversely, DL, an advancement of ML, employs mathematical neural networks to automatically extract features and handle intricate data at the cost of data size and computational complexity. This work aims to provide a state-of-the-art understanding of the tools, data sources and techniques used in MI and their benefits and challenges. We evaluate the growth of MI through its subfields and track the main path of its advancement for artificial intelligence-driven materials discovery. The advancements in computational intelligence via machine learning and deep learning algorithms in different fields of materials science are discussed. As a specific example, understanding of materials properties using microstructural images is reviewed. Future demands and research prospects in materials science utilizing materials informatics have also been comprehensively analyzed.
format Article
author Sivan, Dawn
Kumar, K. Satheesh
Aziman, Abdullah
Raj, Veena
Izan Izwan, Misnon
Ramakrishna, Seeram
Jose, Rajan
author_facet Sivan, Dawn
Kumar, K. Satheesh
Aziman, Abdullah
Raj, Veena
Izan Izwan, Misnon
Ramakrishna, Seeram
Jose, Rajan
author_sort Sivan, Dawn
title Advances in materials informatics: A review
title_short Advances in materials informatics: A review
title_full Advances in materials informatics: A review
title_fullStr Advances in materials informatics: A review
title_full_unstemmed Advances in materials informatics: A review
title_sort advances in materials informatics: a review
publisher Springer
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/40459/1/Advances%20in%20materials%20informatics.pdf
http://umpir.ump.edu.my/id/eprint/40459/2/Advances%20in%20materials%20informatics_FULL.pdf
http://umpir.ump.edu.my/id/eprint/40459/
_version_ 1822924168287485952
score 13.235362

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