Computers “Learn,” Then Predict Materials Properties | December 3, 2015 Issue - Vol. 93 Issue 48 | Chemical & Engineering News
Volume 93 Issue 48 | p. 10 | News of The Week
Issue Date: December 7, 2015 | Web Date: December 3, 2015

Computers “Learn,” Then Predict Materials Properties

Computation: Machine learning algorithm mines data to predict materials characteristics with high speed and improved capabilities
Department: Science & Technology
News Channels: Materials SCENE
Keywords: materials, data mining, machine learning, computation
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The new algorithm mined materials data to predict two glass-forming regions for Al-Ni-Zr alloys, which researchers have observed experimentally.
Credit: Logan Ward
A scheme compares computational results to experimental findings.
 
The new algorithm mined materials data to predict two glass-forming regions for Al-Ni-Zr alloys, which researchers have observed experimentally.
Credit: Logan Ward

Some materials researchers believe that computers are the future: Teach them well, and they’ll help lead the way.

Scientists led by Logan Ward and Chris Wolverton at Northwestern University recently developed a machine-learning algorithm that mines existing data to predict the properties of uncharacterized solid-state materials. The technique could help researchers discover new materials for a range of applications, including energy generation and storage.

The approach can predict basic properties, such as a material’s electronic band gap, roughly 1 million times as fast as conventional computational methods, specifically density functional theory (DFT), according to Ward.

Furthermore, the machine-learning technique makes predictions that DFT cannot, such as alloy compositions that could form metallic glasses, Ward announced last week at the Materials Research Society Fall Meeting & Exhibit in Boston.

But DFT and machine learning aren’t exactly competitors. In fact, DFT is helping teach machine-learning algorithms, Ward told C&EN.

DFT predicts materials properties using calculations based on the electron density within a substance. Over the past five years, Wolverton’s team has developed high-throughput DFT methods to populate a large data­base of materials and their computed properties.

Pit your chemical gumption against the machine learning algorithm in this online game at http://bit.ly/metalgame.
Credit: Wolverton Research Group
A screenshot from an online chemistry game.
 
Pit your chemical gumption against the machine learning algorithm in this online game at http://bit.ly/metalgame.
Credit: Wolverton Research Group

Information from this and other materials databases provides “training” sets that teach a machine learner. These training sets contain computed or measured properties, as well as fundamental attributes of the materials, such as the radii of their atoms, Ward explained.

The algorithm then looks for relationships between these attributes and materials properties to predict the behavior of unexplored materials.

“When it comes to machine learning, what you want is leads. You want hits,” said Jason Hattrick-Simpers, a chemical engineer at the University of South Carolina who was not involved with the project.

At the Boston meeting, Ward unveiled predictions for eight unstudied materials that could form metallic glasses, which impressed Hattrick-Simpers. If researchers analyze these alloys and find the materials don’t behave as predicted, the results still provide the algorithm with better data to refine its forecasting. “Win or lose, it’s a gain for the system,” he said.

The new machine-learning code is free to download, Northwestern’s Wolverton said.

 
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Comments
Matt Davenport (December 11, 2015 1:49 PM)
My apologies to the Wolverton Group and anyone trying to find the talk on which this story is based. Here's some identifying information: "General Machine-Learning-Based Framework for Predicting the Properties of Materials Using Composition" by Logan Ward, Ankit Agrawal, Alok Choudhary, and Chris Wolverton.

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