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Related Experiment Video

Updated: Jul 25, 2025

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

A framework for materials informatics education through workshops.

Arun Mannodi-Kanakkithodi¹, Austin McDannald², Shijing Sun³

¹Materials Engineering, Purdue University, West Lafayette, USA.

|June 26, 2023

Summary

This summary is machine-generated.

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.

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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.