Research Summary
Two critical challenges guide CAMM research.
CAMM addresses how to overcome the complexity of quantum materials that currently hinders progress, and how to realize structural materials capable of the extreme performance characteristics needed for future technologies.
These materials are vital for a broad spectrum of energy, transport, and security applications.
Quantum Materials
AI for quantum magnetic materials and engineered quantum systems
IRG1 focuses on applying AI to quantum magnetic materials and engineered quantum systems supporting the rational design of materials with applications. It develops AI-based tools to handle complex quantum phases and physical behavior.
Learn About IRG1Materials for Extremes
High-performance structural materials for future technologies
IRG2 explores the effects of extreme conditions on stability, structure, and properties of high-performance structural materials, elucidating the materials paradigm for these novel systems.
Learn About IRG2Connected Research
The IRGs work together through integrated experiment, theory, and AI.
Together, CAMM’s research groups connect AI-enabled discovery, quantum materials, and materials for extreme environments through collaborative research workflows.
Broader Impact
Training future researchers in next-generation materials discovery
CAMM includes a tailored graduate education model and curriculum incorporating the use of AI in materials and manufacturing discovery.
CAMM impacts the nation by making new experimental and AI capabilities available to researchers; training future researchers in next-generation approaches to quantum and extreme materials; and advancing the frontier of technologies from low power electronics and quantum sensors to nuclear fusion and hypersonic systems.
Research Resources
Resources for acknowledgments, onboarding, data sharing, and code.
NSF Acknowledgment
Acknowledge MRSEC support
MRSEC support should be acknowledged in any project deliverables, including publications, talks, and software, falling into one of the three categories.
View NSF AcknowledgmentOnboarding & Instructional Videos
How-to videos for newcomers
Onboarding and instructional videos are aimed at newcomers and may be shared broadly as how-to resources.
View Onboarding VideosData & Code Sharing
FAIR Data & Code Sharing
CAMM supports FAIR data and code sharing practices that make research outputs more findable, accessible, interoperable, and reusable. Researchers can use CAMM’s GitHub organization to share project code, documentation, workflows, and related research resources.