Transforming the brightness of beams available to science, medicine, and industry.

CBB's overarching research goal is to increase the intensity, or brightness, of beams of charged particles by a factor of 100 while decreasing the cost of key accelerator technologies. Research within our three themes, Beam Production, Beam Acceleration, and Beam Dynamics and Control converge to achieve these goals, as seen in in our Research Ecosystem.

• All-atom dynamics simulations have become an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. The UF3 framework is built to address this challenge by combinining effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, and as fast as the fastest traditional empirical potentials.
• The UF3 method enables large scale and long time simulations of accelerator materials to identify, e.g., the surface structures and point defect structures and diffusion pathways for photocathodes and superconducting thin-films.
• Repositories at https://github.com/uf3

• GASP is a genetic algorithm for structure and phase prediction written in Python and interfaced to GULP, LAMMPS and VASP. It can search for the structures of clusters, 2D materials, wires, and bulk materials and do both fixed-composition and phase diagram searches.
• For the CBB, we use GASP to determine surface phase diagrams of photocathodes and superconducting thin-films as a function of temperature and pressures to understand their possible properties and identify optimal growth conditions.
• Repository at https://github.com/henniggroup/GASP-python