| Abstract Scope |
Nanostructured metals offer unique opportunities to mitigate harsh environments. However, often, the connectivity between process conditions and the resulting material properties is complex, evading full predictivity via high-fidelity modeling, thereby requiring laborious and time-consuming experimental evaluation. In this work, we adopt an accelerated workflow to detect material structure/composition, prognose associated properties, and adapt the associated process to achieve improved product outcomes. This accelerated detect-prognose-adapt cycle is aided by three key elements: (1) automated combinatorial synthesis to enable rapid parameter sweeps and rich training datasets, (2) high-throughput evaluation of both conventional and surrogate indicators of material chemistry, structure, and properties, and (3) machine learning algorithms to unravel correlations in high-dimensional spaces beyond expert cognition. This approach has unearthed new process conditions and material compositions that offer robust material properties that were previously unavailable. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. |