Our research strengths in looking at the temporal aspects of lifetime data offer a unique perspective that can be used for everything from materials discovery to lifetime performance. Data analysis can be combined with microstructure characterization to understand how either initial processing or evolution during performance results in material properties. Incorporating data science can advance alloy development—by looking at 30 years of data with steel alloys, for example, we can model what type of alloy will best meet a specific set of needs. Through data, we can follow the degradation pathways of polymers in packaging, as well as conduct time-series analyses of power plant performance with regard to energy efficiency.

In the realm of machine learning, we’re applying AI to imagery to better assess everything from the characterization of surfaces to the makeup of super alloys to failure mechanisms. At this interface of statistics, big data and materials science, we analyze, evaluate and enhance materials in smart and strategic ways to fully realize the capabilities of materials systems.