Advanced AI links atomic structure to quantum tech

A research team led by Oak Ridge National Laboratory has developed a new method to uncover the atomic origins of unusual material behavior. This approach uses Bayesian deep learning, a form of artificial intelligence that combines probability theory and neural networks to analyze complex datasets with exceptional efficiency.

The technique reduces the amount of time needed for experiments. It helps researchers explore sample regions widely and rapidly converge on important features that exhibit interesting properties.

"This method makes it possible to study a material's properties with much greater efficiency," said ORNL's Ganesh Narasimha. "Usually, we would need to scan a large region, and then several small regions, and perform spectroscopy, which is very time consuming. Here, the AI algorithm takes control and does this process automatically and intelligently."

The study explored europium zinc arsenide, a magnetic semimetal known for its unique electronic behaviors; however, the method is generalizable across a wide variety of materials. Using advanced scanning tunneling microscopy, the researchers unveiled connections between atomic structures and electronic properties. This streamlined approach simplifies the discovery process and advances the nation's capabilities related to artificial intelligence and quantum science.

The full findings are available in npj Computational Materials. - Scott Gibson