Tag: algorithm

An algorithm for sharper protein films

An algorithm for sharper protein films

Proteins are biological molecules that perform almost all biochemical tasks in all forms of life. In doing so, the tiny structures perform ultra-fast movements. In order to investigate these dynamic processes more precisely than before, researchers have developed a new algorithm that can be used to evaluate measurements at X-ray free-electron lasers such as the SwissFEL more efficiently. They have now presented it in the journal Structural Dynamics.

Sometimes, when using the navigation system while travelling by car, the device will locate you off the road for a short time. This is due to the inaccuracy of the GPS positioning, which can be as much as several metres. However, the algorithm in the sat nav will soon notice this and correct the trajectory displayed on the screen, i.e. put it back on the road.

A comparable principle for addressing unrealistic motion sequences has now been successfully applied by a team of researchers led by PSI physicist Cecilia Casadei. However, their objects of investigation are about a billion times smaller than a car: proteins. These building blocks of life fulfil crucial functions in all known organisms. In doing so, they often perform ultra-fast movements. Analysing these movements precisely is crucial for our understanding of proteins which can help us produce new medical agents, amongst other things.

How to “film” proteins…

To further improve the understanding of protein movements, Casadei, together with other PSI researchers, a researcher at DESY in Hamburg and other colleagues at the University of Wisconsin in Milwaukee, USA, has developed an algorithm that evaluates data obtained in experiments at an X-ray free-electron laser (XFEL). An XFEL is a large-scale research facility that delivers extremely intense and short flashes of laser-quality X-ray light. Here, a method called time-resolved serial femtosecond X-ray crystallography (TR-SFX) can be used to study the ultra-fast movements of proteins.

The measurements are very complex for several reasons: the proteins are much too small to be imaged directly, their movements are incredibly fast, and the intense pulse of X-ray light of an FEL completely destroys the proteins. On the experimental level, TR-SFX already solves all these problems: no individual molecule is measured, but rather a large number of identical protein molecules are induced to grow together in a regular arrangement to form protein crystals. When the FEL X-ray light shines on these crystals, the information is captured in time before the crystals and their proteins are destroyed by the pulse of light. The raw data from the measurements are available as so-called diffraction images: light spots that are created by the regular arrangement of the proteins in the crystal and registered by a detector.

Read more on the PSI website

Image: Physicist Cecilia Casadei was part of an international team that developed a new analysis algorithm. With their method, called “low-pass spectral analysis”, the data collected when proteins are measured at X-ray free-electron lasers can be evaluated more efficiently than before.

Credit: Paul Scherrer Institute/Mahir Dzambegovic

Computer, Is My Experiment Finished?

Computer, Is My Experiment Finished?

Everyone knows that the Computer—an artificial intelligence (AI)-like entity—on a Star Trek spaceship does everything from brewing tea to compiling complex analyses of flux data. But how are they used at real research facilities? How can AI agents—computer programs that can act based on a perceived environment—help scientists discover next-generation batteries or quantum materials? Three staff members at the National Synchrotron Light Source II (NSLS-II) described how AI agents support scientists using the facility’s research tools. As a U.S. Department of Energy’s (DOE) Office of Science user facility located at DOE’s Brookhaven National Laboratory, NSLS-II offers its experimental capabilities to scientists from all over the world who use it to reveal the mysteries of materials for tomorrow’s technology.

From improving experimental conditions to enhancing data quality, Andi Barbour, Dan Olds, Maksim Rakitin, and their colleagues are working on various AI projects at NSLS-II. A recent overview publication in Digital Discovery outlines several—but not all—ongoing AI projects at the facility.

First contact with AI

While movies often show AI agents as sentient super computers that can perform various tasks, real-world AI agents differ greatly from this portrayal.

“What we mean when we say AI is that we come up with an algorithm or a method—basically some mathematical process—that is going to do a ‘thing’ for us, such as classifying, analyzing, or making decisions, but we’re not going to hardcode the logic,” explained Olds, a physicist who works at one of NSLS-II’s scientific instruments that enables a wide range of research projects. The instruments at NSLS-II are called beamlines because they are a combination of an x-ray beam delivery system and an experimental station.

Rakitin, a physicist specialized in developing software to collect or analyze data at NSLS-II, added, “Instead of giving the program—the AI agent—a model, it builds its own model through training. If we want it to recognize a cat, we show it a cat instead of explaining that it is a furry animal with four legs, pointy ears, a tail, and so on. The program has to figure out how to identify a cat by itself.”

Researchers at facilities such as NSLS-II have two main reasons for adapting AI agents to their needs: the sheer volume of data and its complexity. Twenty years ago, it took several minutes to snap a data image—such as a diffraction pattern—of a battery. Now, at the beamline Olds works at, they can take the same shot in a fraction of a second. While this allows more research to happen at the beamline, it outpaces the traditional strategies used to analyze the data.

Barbour, a chemical physicist, faces the second challenge, complex data, in her work studying dynamics in quantum materials. Together with her collaborators, she investigates how the atomic and electronic order in these materials evolve under variable conditions.

“When we do experiments at the beamline, we are looking for correlations and patterns in the data over time. So, if we would need to write one long program that captures all the possibilities of our experiments, it would be incredibly complicated, hard to read, terrible to maintain, and a nightmare to automate. But an AI tool can learn how to handle our complex data without the need to explain every detail to the agent,” Barbour said.

Read more on the Brookhaven National Laboratory website

Image: From left to right: Andi Barbour, Maksim Rakitin, and Dan Olds on the balcony overseeing the experimental floor of the National Synchrotron Light Source II

AI Agent Helps Identify Material Properties Faster

AI Agent Helps Identify Material Properties Faster

High-throughput X-ray diffraction measurements generate huge amounts of data. The agent renders them usable more quickly.

Artificial intelligence (AI) can analyse large amounts of data, such as those generated when analysing the properties of potential new materials, faster than humans. However, such systems often tend to make definitive decisions even in the face of uncertainty; they overestimate themselves. An international research team has stopped AI from doing this: the researchers have refined an algorithm so that it works together with humans and supports decision-making processes. As a result, promising new materials can be identified more quickly.

A team headed by Dr. Phillip M. Maffettone (currently at National Synchrotron Light Source II in Upton, USA) and Professor Andrew Cooper from the Department of Chemistry and Materials Innovation Factory at the University of Liverpool joined forces with the Bochum-based group headed by Lars Banko and Professor Alfred Ludwig from the Chair of Materials Discovery and Interfaces and Yury Lysogorskiy from the Interdisciplinary Centre for Advanced Materials Simulation. The international team published their report in the journal Nature Computational Science from 19 April 2021.

Read more on the BNL website

Image: Daniel Olds (left) and Phillip M. Maffettone working at the beamline.

Credit: BNL