Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations
Data science and material informatics are gaining traction in alloy design. This is due to increasing infrastructure, computational capabilities and established open-source composition-structure-property databases increasingly becoming available. Additionally, the popularization of data science tech...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Results in Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590048X23000936 |
| _version_ | 1827815998569316352 |
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| author | D.E.P. Klenam T.K. Asumadu M. Vandadi N. Rahbar F. McBagonluri W.O. Soboyejo |
| author_facet | D.E.P. Klenam T.K. Asumadu M. Vandadi N. Rahbar F. McBagonluri W.O. Soboyejo |
| author_sort | D.E.P. Klenam |
| collection | DOAJ |
| description | Data science and material informatics are gaining traction in alloy design. This is due to increasing infrastructure, computational capabilities and established open-source composition-structure-property databases increasingly becoming available. Additionally, the popularization of data science techniques and the drive to reduce overall material life-cycle cost by ∼60% have necessitated increased use of the technique. Alloy design is a multi-optimization problem hence the Edisonian approach is no more viable from cost, labour, and time-to-market perspectives. Although, there have been successful application of data science and material informatics in alloy design, there are drawbacks. This review provides a critical assessment of limitations associated with data science and materials informatics to alloy discovery and property characterization. Among these are cost, false positives, over – and underestimation of properties, lack of experimental data to validate simulated results, lack of state-of-the-art facilities in most developing countries and uncertainty modelling. The implications and areas for future research directions are highlighted. |
| first_indexed | 2024-03-12T00:08:39Z |
| format | Article |
| id | doaj.art-e839587dea68445e97acdddea1ec8205 |
| institution | Directory Open Access Journal |
| issn | 2590-048X |
| language | English |
| last_indexed | 2024-03-12T00:08:39Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Materials |
| spelling | doaj.art-e839587dea68445e97acdddea1ec82052023-09-16T05:31:37ZengElsevierResults in Materials2590-048X2023-09-0119100455Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitationsD.E.P. Klenam0T.K. Asumadu1M. Vandadi2N. Rahbar3F. McBagonluri4W.O. Soboyejo5Academic Development Unit & School of Chemical and Metallurgical Engineering, University of the Witwatersrand, 1 Jan Smuts Avenue, WITS, 2001, Johannesburg, South Africa; Department of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, M.A., 06109, USA; Corresponding author. Academic Development Unit and School of Chemical and Metallurgical Engineering, University of the Witwatersrand, 1 Jan Smuts Avenue, WITS, 2001, Johannesburg, South Africa.Department of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, M.A., 06109, USA; Department of Materials Engineering, Sunyani Technical University, Box 206, Sunyani, GhanaDepartment of Civil Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, M.A., 06109, USADepartment of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, M.A., 06109, USA; Department of Civil Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, M.A., 06109, USADepartment of Mechanical Engineering, Academic City University College, Haatso, Accra, GhanaDepartment of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, M.A., 06109, USAData science and material informatics are gaining traction in alloy design. This is due to increasing infrastructure, computational capabilities and established open-source composition-structure-property databases increasingly becoming available. Additionally, the popularization of data science techniques and the drive to reduce overall material life-cycle cost by ∼60% have necessitated increased use of the technique. Alloy design is a multi-optimization problem hence the Edisonian approach is no more viable from cost, labour, and time-to-market perspectives. Although, there have been successful application of data science and material informatics in alloy design, there are drawbacks. This review provides a critical assessment of limitations associated with data science and materials informatics to alloy discovery and property characterization. Among these are cost, false positives, over – and underestimation of properties, lack of experimental data to validate simulated results, lack of state-of-the-art facilities in most developing countries and uncertainty modelling. The implications and areas for future research directions are highlighted.http://www.sciencedirect.com/science/article/pii/S2590048X23000936Alloy developmentComputational alloy designMachine learningMaterial informaticsRational alloy design |
| spellingShingle | D.E.P. Klenam T.K. Asumadu M. Vandadi N. Rahbar F. McBagonluri W.O. Soboyejo Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations Results in Materials Alloy development Computational alloy design Machine learning Material informatics Rational alloy design |
| title | Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations |
| title_full | Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations |
| title_fullStr | Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations |
| title_full_unstemmed | Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations |
| title_short | Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations |
| title_sort | data science and material informatics in physical metallurgy and material science an overview of milestones and limitations |
| topic | Alloy development Computational alloy design Machine learning Material informatics Rational alloy design |
| url | http://www.sciencedirect.com/science/article/pii/S2590048X23000936 |
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