Critical mineral and material separations research seeks new ways to glean important elements and other materials from complex and unconventional feedstocks.

Supply chain modeling for critical minerals and materials encompasses a set of analytical, AI, and scenario modeling strategies for mapping, tracking, and simulating risks and potential future outcomes for these supply chains.

Machine learning engineering applies AI techniques to build scalable systems, driving innovation in data processing, automation, and predictive modeling.

Quantum information science studies quantum mechanics to advance computing, secure communication, and solve challenges beyond classical systems.

Cyber resilience emphasizes strategies to defend against cyber threats, ensuring reliable operations, data security, and recovery in critical systems.

Critical infrastructure protection safeguards essential systems like power, water, and transportation against threats, ensuring resilience and national security.

Export controls regulate the trade of sensitive goods, technologies, and information to protect national security and support compliance with global trade laws.

Graph analytics analyzes relationships within complex datasets to uncover patterns, enhance decision-making, and improve network system efficiency.

Physics-informed machine learning integrates scientific laws with AI, improving predictions, modeling, and solutions for complex scientific challenges.

Computing testbeds offer experimental platforms for testing advanced technologies, driving innovation in computing performance and scalability.

Deep reinforcement learning employs AI to solve complex problems by learning optimal strategies, revolutionizing autonomous systems and decision-making.