SLAC researchers pioneer new methods in ultrafast science for sharp molecular films

Newswise — Imagine being able to watch the inner workings of a chemical reaction or material as it changes and reacts to its environment — it’s the kind of thing researchers can do with a high-speed “electron camera.” which is called megaelectronvolt ultrafast electron diffraction (MeV-UED) on equipment linac coherent light source (LCLS) at the US Department of Energy SLAC National Accelerator Laboratory,

Now, in two new studies, researchers from SLAC, Stanford, and other institutions have figured out how to capture those tiny, ultrafast details with greater accuracy and efficiency. In first studyrecently published structural dynamics, a team invented a technique to improve time resolution for the electron camera. in moment, published In nature communicationThe researchers trained and used artificial intelligence (AI) to tune the MeV-UED electron beam and adapt it to different experimental needs.

“These effects are profound enough to advance beam instrumentation and diagnostics for the SLAC electron accelerator and mark a new frontier in the discovery of novel effects with unprecedented precision,” said Mohammad Othman, an associate scientist at SLAC and co-author of both papers. Will enable.”

timing is everything

Chemical reactions occur rapidly – ​​sometimes critical events occur in a millionth of a billionth of a second, or femtosecond. Capturing these femtosecond events is known as a field geodesic. ultrafast science This requires some of the most advanced scientific equipment in the world – instruments like the MeV-UED.

MeV-UED takes snapshots by hitting the samples with a beam electrons And recording what happens to the material as electrons pass through it. The result is a molecular movie that allows scientists to observe the behavior of molecules and atoms at ultrafast speeds and gain insight into processes that are important for energy solutions and innovative new materials and medicines, among other things.

The tricky thing is, the MeV-UED beam is made up of clusters of electrons, or electron pulses – and they can be an unruly bunch. When the electron pulses reach the material sample, there is a slight spread in the arrival time between the first electron and the last electron of the pulse. This time spread, as well as variations in the timing between pulses, called jitter, makes it difficult to tell when things happen in each electron camera image.

SLAC Team previously reported That using terahertz radiation, which lies between microwave and infrared light on the electromagnetic spectrum, and adding a compressor to the MeV-UED has improved the time resolution of the instrument. The compressor uses terahertz radiation to reduce the propagation time of the electron pulse through a method called – appropriately – bunch compression.

In their quest to further tame electron bunches, the team combined bunch compression with another method called time stamping: after the pulse interacts with the sample and hits the detector, timing information is encoded into the electron camera image. Is performed. Through a simple time type, users can more accurately set the time of each image or movie.

The combination of bunch compression and time stamping increased timing accuracy and reduced jitter. “Researchers can use this technique to observe extremely fast time scales, particularly atomic motion in materials,” Othman said. “This atomic microscope can be used in fundamental sciences: materials science, chemistry, green energy, quantum information and more. Achieving femtosecond scale is important to investigate these science areas.

With the success of this prototype, their next step is to create a device with combined capabilities. “For example, in terms of timing we are trying to push the limits of what MeV-UED can do. Because MeV-UED is part of a DOE user facility, we wanted to build this tool that can be an option for users,” Othman said.

power of ai

Researchers from all over the world come to SLAC’s MEV-UED to run their experiments, and their needs vary widely. For each experiment, beam operators need to optimize 20–30 parameters, such as beam spot size, and consider trade-offs between all parameters. SLAC staff scientist and paper lead author Fuhao Ji compared the tuning process to changing recipe ingredients when baking bread to suit a customer’s taste — there are a lot of factors to consider, and everyone has slightly different tastes.

Currently, experienced operators select all the options themselves with the help of an automated process, but this is not as efficient as it could be. To make this run more smoothly, SLAC researchers on the laboratory’s accelerator and instrument sides teamed up with the laboratory’s AI experts to implement a special AI model, called Multi-Objective Bayesian Optimization (MOBO), which simulates electronegativity. Tunes directly online. Beam on MeV-UED. This approach can be at least ten times faster than an experienced operator as well as an automated process. Since users have a fixed amount of beam time, this means they will have less time and more time running their experiments and collecting data.

Before turning the AI ​​model loose, the SLAC team had to train it so it knew not only what to look for, but how to evaluate trade-offs between beam parameters. The model learned by doing: The researchers conducted experiments and collected data as they usually do, then fed that data into the model, which revealed how different parameters interact to shape the beam.

Like other AI models, MOBO can predict new results from novel parameter settings, making it particularly useful when a researcher needs a beam setting that has not been used before. The model also provides a more comprehensive picture of the experimental system.

“This is the result of close collaboration between MeV-UED and the Accelerator Directorate Machine Learning Group and paves the way to the ultimate goal of establishing an end-to-end automated intelligent scientific user facility at MeV-UED,” Ji said, Where AI algorithms will co-optimize all components in the entire system, from the electron source to the accelerator, light source, sample settings and detector.

Gee and colleagues are looking to expand the capabilities of the MOBO tool. Their next step is to adopt another AI tool, Bayesian algorithm execution, to further speed up the optimization process and achieve better performance.

“We expect it to have a broad impact on research in diverse disciplines such as physics, chemistry, biology and quantum materials at large-scale, complex scientific user facilities,” Ji said.

The research was supported by the DOE Office of Science and SLAC’s Laboratory Directed Research and Development program. LCLS is a DOE Office of Science User Facility.