Machine learning algorithms can sometimes do a great job with a little help from human expertise, at least in the field of materials science.
In many specialized areas of science, engineering and medicine, researchers are turning to machine learning algorithms to analyze data sets that have grown too large for humans to understand. In materials science, success with this effort could accelerate the design of next-generation advanced functional materials, where development now usually depends on old-fashioned trial and error.
By themselves, however, data analytics techniques borrowed from other research areas often fail to provide the insights needed to help materials scientists and engineers choose which of many variables to adjust — and the techniques can’t account for dramatic changes such as the introduction of a new chemical compound into the process.
In a paper published in the journal NPJ Computational Materials, researchers explain a technique known as dimensional stacking, which shows that human experience still has a role to play in the age of machine intelligence. The machines gain an edge at solving a challenge when the data to be analyzed are intelligently organized based on human knowledge of what factors are likely to be important and related.
“When your machine accepts strings of data, it really does matter how you are putting those strings together,” said Nazanin Bassiri-Gharb, the paper’s corresponding author and a scientist at the Georgia Institute of Technology. “We must be mindful that the organization of data before it goes to the algorithm makes a difference. If you don’t plug the information in correctly, you will get a result that isn’t necessarily correlated with the reality of the physics and chemistry that govern the materials.”
The research is funded by NSF’s Division of Materials Research.
