Abstract
Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials consisting of SiO2, SiC, and Poly(methyl methacrylate). Empirical computing time of our quantum-classical hybrid algorithm involving a factorization machine, a rigorous coupled wave analysis, and a D-Wave 2000Q quantum annealer was insensitive to the problem size, while a classical counterpart experienced rapid increase. Our method was used to design complex structures of wavelength selective radiators showing much better concordance with the thermal atmospheric transparency window in comparison to existing human-designed alternatives. Our result shows that quantum annealing provides scientists gigantic computational power that may change how materials are designed.
Highlights
Further evolution in materials that controls energy carriers, such as photons, electrons, and phonons, is a condition to realize sustainable industry and society
Note that if the FOMs of all structures are evaluated by the rigorous coupled wave analysis (RCWA) simulations for the 18 bits case to identify the best structure, the required time is more than ten times longer than the algorithm where the exhaustive search method is used for the selection part
The candidate material, which is selected with respect to the acquisition function, is represented as a solution to a combinatorial optimization by using an factorization machine (FM), and this optimization problem is solved by a quantum annealer
Summary
Further evolution in materials that controls energy carriers, such as photons, electrons, and phonons, is a condition to realize sustainable industry and society. Using the observed properties of the selected candidate, the machine learning model is updated and defines a different acquisition function for the iteration Repeating this procedure with the aid of the machine learning should drastically reduce the number of experimental investigations to design materials with the desired properties. Engineered thermal-emission leads to high-efficiency thermophotovoltaics [36,37], incandescent light sources [38], biosensing [39,40], microbolometers [41,42], imaging [43], and drying furnace [44] Another application that has recently attracted much attention, in response to concerns of global warming and energy crises, is radiative sky cooling that utilizes the untapped 3 K cold space as a heat sink. Our proposed optimization method is used to design such a radiator with a wavelength selectivity higher than previously designed ones
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