Accelerated Development of Refractory Nanocomposite Solar Absorbers using Bayesian Optimization

Machine learning-based approach is desired for accelerating materials design, development and discovery in combination with high-throughput experiments and simulation. In this work, we propose to apply a Bayesian optimization method to design ultrathin multilayer tungsten-silicon carbide (W-SiC) nanocomposite absorber for high-temperature solar power generation. Based on a semi-analytical scattering matrix method, the design of spectrally selective absorber is optimized over a variety of layer thicknesses to maximize the overall solar absorptance. Our nanofabrication and experimental characterization results demonstrate the capability of the proposed approach for accelerated development of refractory light-absorbing materials. Comparison with other global optimization methods, such as random search, simulated annealing and particle swarm optimization, shows that the Bayesian optimization method can expedite the design of multilayer nanocomposite absorbers and significantly reduce the development cost. This work sheds light on the discovery of novel materials for solar energy and sustainability applications.