AI identifies change in microstructure in aging materials

Lawrence Livermore National Laboratory (LLNL) scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using AI. The work recently appeared online in the journal Computational Materials Science. Technological progress in materials science applications spanning electronic, biomedical, alternate energy, electrolyte, catalyst design and beyond is often hindered by a lack of understanding of complex relationships between the underlying material microstructure and device performance. But AI-driven data analytics provide opportunities that can accelerate materials design and optimization by elucidating processing-performance correlations in a mathematically tractable way. However, to reliably train large networks one needs data from tens of thousands of samples, which, unfortunately is often prohibitive in new systems and new applications due to the cost of sample-preparation and data collection.