WiDS Livermore 2024 | Technical Talk 1

On March 13, 2024, LLNL held a Women in Data Science (WiDS) regional event featuring speakers, panelists, networking and mentoring sessions, and other activities. Learn more about WiDS Livermore at data-science.llnl.gov/wids.
Laura Bruckman from Case Western Reserve University presented "Materials Data Science Approach for Reliability: Materials to Systems." Lifetime prediction of long-lived materials requires an understanding of key degradation mechanisms in relation to the stressors (i.e., UV irradiance, water, temperature, mechanical stress) and the applied stressor levels, an understanding accessible through application of a materials data science approach. Predicting durability and mitigating degradation has failed under traditional materials reliability methods, which has typically focused on pass/fail criteria for materials under accelerated exposures. When a material fails, the necessary data to understand the failure is often missing since detailed evaluations of a large enough population of samples were not performed in the reliability study. Commercial PV modules are a complex system made up of several different materials. The degradation of one material impacts the next material especially at the interface. This has required a data science approach to collecting the complex data including the real-world stress conditions for each site, the time-series power data, degradation data of individual modules, and even on different materials. This data needs to be FAIRified, integrated, and modeled. This then allows for the prediction of the lifetime of modules in different climate zones and also for power prediction.
Bruckman is an Associate Professor in the Department of Materials Science and Engineering in the Case School of Engineering, Case Western Reserve University. Her research is focused on a data science approach to materials degradation. She is an expert in leveraging quantitative spectroscopic techniques and image analysis to understand materials degradation under different stressors. Her research has application to solar packaging materials, building envelope materials, coatings, and additively manufactured materials. She teaches in the Applied Data Science program at CWRU with a focus on visualization and analytics, research projects, and communicating results to various audiences.
LLNL-VIDEO-863720