Gaps and Barriers to Successful Integration and Adoption of Practical Materials Informatics Tools and Workflows

Preparation of the future materials workforce consistent with major imperatives rooted in integrated computational materials engineering (ICME) and the materials genome initiative (MGI) is most effectively pursued within the vision of a materials innovation ecosystem that spans across conventional e...

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Veröffentlicht in:JOM (1989) Jg. 73; H. 1; S. 138 - 148
1. Verfasser: McDowell, David L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2021
Springer Nature B.V
Schlagworte:
Additive manufacturing
Artificial intelligence
Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification
Chemistry/Food Science
Data science
Earth Sciences
Education
Engineering
Environment
Experiments
Genomes
Informatics
Initiatives
Innovations
Materials engineering
Materials information
Materials science
Physics
R&D
Research & development
Simulation
Skills
Systems engineering
Workforce
United States > US
Georgia
ISSN:1047-4838, 1543-1851
Online-Zugang:Volltext
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Zusammenfassung:Preparation of the future materials workforce consistent with major imperatives rooted in integrated computational materials engineering (ICME) and the materials genome initiative (MGI) is most effectively pursued within the vision of a materials innovation ecosystem that spans across conventional engineering, science, and computing disciplines. The ICME foundation integrates principles of materials science with computational methods, including increasing reliance on modern data science methods that are savvy to digital information that recognizes hierarchical material structure and the need for correlative relations for process-structure and structure-property relations. We consider gaps in academic research and education programs related to systems engineering, uncertainty quantification of both experiments and computation, and data science methods. Barriers to the introduction of materials data science are discussed, as well as opportunities for innovation in educating the future MGI and ICME workforce.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1047-4838
1543-1851
DOI:10.1007/s11837-020-04434-8