Mo, Mianzhen | Search NASA

DOE OSTI2024

Response of fs-Laser-Irradiated Diamond by Ultrafast Electron Diffraction

Structural details of the proposed solid–liquid phase transition of carbon have remained elusive, despite years of study. While it is theorized that novel carbon materials form from a liquid precursor, experimental studies have lacked the temporal and spatial resolution necessary to fully characterize the purported liquid state. Here, in this work, we utilize megaelectronvolt-ultrafast electron diffraction (MeV-UED) to study laser irradiated submicron diamond thin films in a pump–probe scheme with picosecond time resolution to visualize potential structural changes of excited diamond. We probe the structure of diamond using a combination of fluences (13, 40 J/cm 2 ) and time delays (10, 25, 100 ps), but observe negligible changes in the static diffraction pattern of diamond and an overall decrease in diffraction intensity up to 100 ps after the excitation pulse. We thus conclude that no appreciable amount of liquid or graphitized carbon is present and highlight the structural resilience of bulk diamond to intense 800 nm ultrafast laser pulses.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

43 PARTICLE ACCELERATORS↗