Affiliation:
1. School of Mechanical Engineering and the Birck Nanotechnology Center Purdue University West Lafayette 47907 USA
2. School of Materials Engineering Purdue University West Lafayette 47907 USA
3. School of Electrical and Computer Engineering Purdue University West Lafayette 47907 USA
4. School of Materials Engineering School of Electrical and Computer Engineering Purdue University West Lafayette 47907 USA
Abstract
AbstractMachine learning (ML) methods have gained widespread attention in optimizing nanostructures for target transport properties and discovering unexpected physics. Here, an ML method is developed to discover and experimentally confirm binary CeO2‐MgO aperiodic multilayers (AMLs) for thermal barrier coatings with significantly enhanced reflectance. The effect of varying AML design parameters like total thickness, average period, and randomness in layer thicknesses, on the spectral and total reflectance is demonstrated. Introducing aperiodicity in layer thicknesses is shown to lead to a broadband increase in spectral reflectance due to photon localization. Since the number of possible AML structures increases exponentially with total thickness, a Genetic Algorithm optimizer is developed to efficiently discover AMLs with enhanced reflectance, for total thicknesses of 5–50 µm. Surprisingly, all the optimized structures show an odd number of layers with a CeO2 layer at both ends, deviating from the traditional way of designing binary superlattices with paired layers. The optimized AML and a reference periodic superlattice of 5 µm thickness are fabricated by Pulsed Laser Deposition and characterized by optical reflectance measurements. The fabricated AML, despite considerable fabrication uncertainty, still enhances the reflectance to 48% from 40% of the reference superlattice, validating the effectiveness of our ML‐based optimization process.
Funder
Defense Sciences Office, DARPA
Subject
Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials