AI for Materials Discovery

New, lucrative  and very interesting space

Artificial Intelligence for Materials Discovery

By Don Monroe

Communications of the ACM, April 2023, Vol. 66 No. 4, Pages 9-11   10.1145/3583080

3D chemical compounds floating in space, illustration  ... 

The software-driven successes of deep learning have been profound, but the real world is made of materials. Researchers are turning to artificial intelligence (AI) to help find new materials to provide better electronics and transportation, and the energy to run them.

Despite its undeniable power, however, "Machine learning, especially the deep learning revolution, relies heavily on large amounts of data," said Carla Gomes, a computer scientist at Cornell University. "This is not how science works. [Scientists] don't just memorize things."

"Machine learning as we know it is not enough for scientific discovery," she said. "We still have a long way to go."

Nevertheless, researchers are off to a promising start in addressing materials science.

Combinatorial Explosion

One of the challenges in materials discovery is the astronomical number of compositions that might have interesting properties. "High-entropy alloys" (HEA), for example, combine four or more metals. "If you consider all the elements in the periodic table and you will find that you have many combinations, then infinite combinations of the different elements, so that makes prediction very difficult," explained Ziyuan Rao, a postdoc at the Max Planck Institute for Iron Research in Düsseldorf, Germany.

Nonetheless, Rao and his colleagues created a multistage analysis to search for alloys with low thermal expansion, which are important for cryogenic storage of liquified natural gas and for other purposes. The analysis draws on extensive materials datasets, but the available compositions are a tiny, sparse subset of the universe of perhaps 1050 possibilities.

After training a machine-learning model with this data, the researchers used it to select promising candidates, often completely novel. They then used computationally intensive density-functional theory (DFT) calculations to get more precise estimates of each compound's properties. DFT is a widely used shortcut around full quantum mechanical theory. In fact, researchers at DeepMind recently used deep learning to let DFT determine how electron charge is distributed between competing atoms, a longstanding challenge.

A key feature of the HEA search is active learning, which suggests new compositions to examine that will be most informative. "it's a little different from traditional machine learning," Rao said, which typically aims to increase the accuracy of the model. "We also want to use this model to predict new materials with very good properties."

Indeed, Rao and his colleagues further refined their search by experimentally making and measuring some of the best candidates. "You need real-world data," he said, because "Simulated data sometimes is inaccurate." The experimental results are folded back into the modeling, and the loop is repeated six times. The study successfully identified two new alloy compositions with a tiny thermal expansion coefficient, less than two parts per million per degree.  ... '