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A framework for materials informatics education through workshops.
Arun Mannodi-Kanakkithodi1, Austin McDannald2, Shijing Sun3
1Materials Engineering, Purdue University, West Lafayette, USA.
MRS Bulletin
|June 26, 2023
Summary
Materials informatics education is crucial for training future scientists in data science, artificial intelligence (AI), and machine learning (ML). Hands-on workshops effectively introduce researchers to AI/ML tools for materials data analysis.
Area of Science:
- Materials Science
- Data Science
- Computational Science
Background:
- The field of materials informatics requires scientists skilled in data science, AI, and ML.
- Traditional curricula often lack comprehensive training in these areas.
Purpose of the Study:
- To highlight the importance of materials informatics education.
- To detail the structure and success of AI/ML workshops for materials researchers.
- To advocate for integrating AI/ML training into materials science education.
Main Methods:
- Conducted hands-on workshops on AI/ML for materials data at MRS Meetings in 2022.
- Focused on essential AI/ML concepts, algorithms, and practical application.
- Utilized competitions to enhance engagement and learning.
Main Results:
- Successfully introduced materials researchers to AI/ML tools and concepts.
- Demonstrated the effectiveness of workshops in facilitating practical application of AI/ML.
- Received positive feedback and plans for future workshop integration.
Conclusions:
- Hands-on workshops are an effective method for materials scientists to learn and apply AI/ML.
- Continued education in AI/ML is vital for the advancement of materials informatics.
- Workshops provide a scalable and accessible platform for disseminating AI/ML knowledge.
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