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Charles Clark

Adjunct Professor

Emeritus Fellow
Charles W. Clark, official portrait, National Institute of Standards and Technology. Work of U.S. Government not subject to copyright.

Contact Information

UMD

Email:
cwc@umd.edu
Office:

University of Maryland
2104 Atlantic Building #224
College Park, MD 20742

Office Phone:
(301) 405-9139

NIST

Office:

National Institute of Standards and Technology
B168 Metrology Building
Gaithersburg, MD 20899

Additional Info

CV:
CWCVita20250417DS.pdf

Research Groups

Recent Publications

Recent News

  • An orange beam goes into a series of curved gratings and on the other side forms two strings of ovals that create a pattern at an x labeled detector.

    Curved Neutron Beams Could Deliver Benefits Straight to Industry

    April 17, 2025

    In a physics first, researchers have created beams of neutrons that travel in curves. The team created these Airy beams (named for English scientist George Airy) using a custom-built device. The beams could enhance neutrons’ ability to reveal useful information about materials ranging from pharmaceuticals to perfumes to pesticides—in part because the beams can bend around obstacles.

  • An array of black and white dots are connected by lines forming a chaotic network. A woman balances on one foot on one of the dots.

    Nobel Prize Celebrates Interplay of Physics and AI

    October 11, 2024

    On Tuesday, the Nobel Prize in physics was awarded to John Hopfield and Geoffrey E. Hinton for their foundational discoveries and inventions that have enabled artificial neural networks to be used for machine learning—a widely used form of AI. The award highlights how the field of physics is intertwined with neural networks and the field of AI.

  • A blue wave is connected by while lines to a grid of white spheres below it.

    Attacking Quantum Models with AI: When Can Truncated Neural Networks Deliver Results?

    May 20, 2024

    Physicists are exploring the opportunities that arise when the power of machine learning—a widely used approach in AI research—is brought to bear on quantum physics. Quantum physics often needs a description that approximately describes many interacting quantum particles. Two researchers at JQI presented new mathematical tools that will help researchers use machine learning to get such approximations and have identified new opportunities in quantum research where machine learning can be applied.