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

Game on: Science Edition

After AIs mastered Go and Super Mario, Brookhaven scientists have taught them how to “play” experiments at NSLS-II

Inspired by the mastery of artificial intelligence (AI) over games like Go and Super Mario, scientists at the National Synchrotron Light Source II (NSLS-II) trained an AI agent – an autonomous computational program that observes and acts – how to conduct research experiments at superhuman levels by using the same approach. The Brookhaven team published their findings in the journal Machine Learning: Science and Technology and implemented the AI agent as part of the research capabilities at NSLS-II.

As a U.S. Department of Energy (DOE) Office of Science User Facility located at DOE’s Brookhaven National Laboratory, NSLS-II enables scientific studies by more than 2000 researchers each year, offering access to the facility’s ultrabright x-rays. Scientists from all over the world come to the facility to advance their research in areas such as batteries, microelectronics, and drug development. However, time at NSLS-II’s experimental stations – called beamlines – is hard to get because nearly three times as many researchers would like to use them as any one station can handle in a day—despite the facility’s 24/7 operations.

“Since time at our facility is a precious resource, it is our responsibility to be good stewards of that; this means we need to find ways to use this resource more efficiently so that we can enable more science,” said Daniel Olds, beamline scientist at NSLS-II and corresponding author of the study. “One bottleneck is us, the humans who are measuring the samples. We come up with an initial strategy, but adjust it on the fly during the measurement to ensure everything is running smoothly. But we can’t watch the measurement all the time because we also need to eat, sleep and do more than just run the experiment.”

Read more on the Brookhaven website

Image: NSLS-II scientists, Daniel Olds (left) and Phillip Maffettone (right), are ready to let their AI agent level up the rate of discovery at NSLS-II’s PDF beamline.

Credit: Brookhaven National Lab

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