Smarter experiments for faster materials discovery

Scientists created a new AI algorithm for making measurement decisions; autonomous approach could revolutionize scientific experiments.

A team of scientists from the U.S. Department of Energy’s Brookhaven National Laboratory and Lawrence Berkeley National Laboratory designed, created, and successfully tested a new algorithm to make smarter scientific measurement decisions. The algorithm, a form of artificial intelligence (AI), can make autonomous decisions to define and perform the next step of an experiment. The team described the capabilities and flexibility of their new measurement tool in a paper published on August 14, 2019 in Nature Scientific Reports.

From Galileo and Newton to the recent discovery of gravitational waves, performing scientific experiments to understand the world around us has been the driving force of our technological advancement for hundreds of years. Improving the way researchers do their experiments can have tremendous impact on how quickly those experiments yield applicable results for new technologies.

>Read more on the NSLS-II at Brookhaven Lab website.

Image: (From left to right) Kevin Yager, Masafumi Fukuto, and Ruipeng Li prepared the Complex Materials Scattering (CMS) beamline at NSLS-II for a measurement using the new decision-making algorithm, which was developed by Marcus Noack (not pictured).

Students use AI for sample positioning at BioMAX

The samples at BioMAX beamline are very sensitive biomolecule crystals. It could, for example, be one of the many proteins you have in your body. They only last for a short time in the intense X-ray light before being damaged and needs to be placed exactly right before the researchers switch on the beam. In their masters’ project, Isak Lindhé, and Jonathan Schurmann have used methods of artificial intelligence to train the computer how to do it.

Hundreds of thousands of proteins
You have hundreds of thousands of different proteins in your body. They do everything from transporting oxygen in your blood to letting your cells take up nutrients after you’ve eaten or make your heart beat. And when things go wrong, you get prescribed medication. The pharmaceutical molecules connect to the proteins in your body to change how they work. To develop new pharmaceuticals with few side effects, the researchers, therefore, need to understand what different proteins look like in detail.

A tedious task
To get high-quality data from a sample it needs to be correctly positioned in the X-ray beam. The conventional model for finding the right position is to scan the sample in the beam to optimize the position. At MAX IV, the X-ray light is very intense, which is good because smaller crystals can be used. But at the same time, very often the sample can’t be scanned in the beam since it would be damaged long before the right position is found. The researchers, therefore, have to perform the rather tedious task of positioning it manually.

>Read more on the MAX IV Laboratory website

Scientists use machine learning to speed discovery of metallic glass

In a new report, they combine artificial intelligence and accelerated experiments to discover potential alternatives to steel in a fraction of the time.

Blend two or three metals together and you get an alloy that usually looks and acts like a metal, with its atoms arranged in rigid geometric patterns.

But once in a while, under just the right conditions, you get something entirely new: a futuristic alloy called metallic glass that’s amorphous, with its atoms arranged every which way, much like the atoms of the glass in a window. Its glassy nature makes it stronger and lighter than today’s best steel, plus it stands up better to corrosion and wear.

Even though metallic glass shows a lot of promise as a protective coating and alternative to steel, only a few thousand of the millions of possible combinations of ingredients have been evaluated over the past 50 years, and only a handful developed to the point that they may become useful.

Now a group led by scientists at the Department of Energy’s SLAC National Accelerator Laboratory, the National Institute of Standards and Technology (NIST) and Northwestern University has reported a shortcut for discovering and improving metallic glass – and, by extension, other elusive materials – at a fraction of the time and cost.

>Read more on the SLAC website

Image: Fang Ren, who developed algorithms to analyze data on the fly while a postdoctoral scholar at SLAC, at a Stanford Synchrotron Radiation Lightsource beamline where the system has been put to use.
Credit: Dawn Harmer/SLAC National Accelerator Laboratory