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Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm
B L DeCost1, J R Hattrick-Simpers1, Z Trautt1
1National Institute of Standards and Technology, Gaithersburg, MD, USA.
Machine Learning: Science and Technology
|March 3, 2021
Summary
Scientific Artificial Intelligence (SciAI) offers opportunities for materials science advancement. Prioritizing scientific, technical, and social advancements is key to leveraging AI for materials-limited technologies.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Chemistry
- Scientific Artificial Intelligence (SciAI)
Background:
- Increasing adoption of machine learning (ML) and artificial intelligence (AI) in scientific research.
- Growing need for advanced materials to overcome technological limitations.
Purpose of the Study:
- Identify key opportunities for developing and utilizing Scientific AI (SciAI) in materials science.
- Propose paths forward for integrating SciAI into research and industry.
- Address scientific, technical, and social aspects of SciAI implementation.
Main Methods:
- Perspective article format.
- Review and synthesis of current trends in AI and materials science.
- Categorization of opportunities and proposed paths forward.
Main Results:
- Identified key scientific/technical opportunities, such as developing robust, multiscale material representations.
- Highlighted social opportunities, including fostering an AI-ready workforce.
- Outlined paths forward from infrastructure development to industry deployment.
Conclusions:
- Strategic prioritization of SciAI opportunities is crucial for advancing materials science.
- A multi-faceted approach encompassing technical and social factors is necessary for successful SciAI integration.
- SciAI offers a credible pathway to overcome current materials limitations and drive technological innovation.
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