Deep learning helps researchers understand new 2-D materials being discovered
Rice University engineers have developed faster techniques to model atom-flat materials for bottom-up design.
Scientists are discovering new 2-D materials at a rapid pace, but they don’t always immediately know what those materials can do.
Researchers at Rice University’s say they can find out fast by feeding basic details of their structures to “deep learning” agents that have the power to map the materials’ properties. Better yet, the agents can quickly model materials scientists are thinking about making to facilitate the “bottom-up” design of 2-D materials.
Rice University researchers used a microstructure model of radiation-damaged hexagonal boron nitride to help them study the benefits of deep learning techniques in simulating two-dimensional materials to understand their characteristics. Courtesy: Prabhas Hundi, Rice University[/caption]
They ran thousands of deep learning simulations on 80 combinations of radiation and temperature for hexagonal boron nitride and 48 combinations for graphene, hitting each combination with 31 random doses of simulated radiation. For some, the researchers trained the deep learning agent with a maximum of 45 percent of data from their molecular dynamics study, achieving up to 97 percent accuracy in predicting defects and their effects on the material’s characteristics.
Adapting trained agents to different materials, they found, required only about 10 percent of the simulated data, greatly speeding up the process while retaining good accuracy.
“We tried to figure out the corresponding residual strengths of the materials after exposure to extreme conditions, along with all the defects,” he said. “As expected, when the mean temperature or the radiation were too high, the residual strength became pretty low. But that trend wasn’t always obvious.”
In some cases, he said, the combined higher radiation and higher temperatures made a material more robust instead of less, and it would help researchers to know that before making a physical product.
“Our deep learning method on the development of structure-property maps could open up a new framework to understand the behavior of 2-D materials, discover their non-intuitive commonalities and anomalies, and eventually better design them for tailored applications,” Shahsavari said.