Transparent radiative coolers can be used as window materials to reduce cooling energy needs for buildings and automobiles, which may contribute significantly to addressing climate change challenges. However, it is difficult to achieve high visible transparency and radiative cooling performance simultaneously. Here, we design a visually transparent radiative cooler on the basis of layered photonic structures using a quantum computing-assisted active learning scheme, which combines active data production, machine learning, and quantum annealing in an iterative loop. We experimentally fabricate the designed cooler and demonstrate its cooling effect. This cooler may lead to an annual energy saving of up to 86.3 MJ/m 2 in hot climates compared with normal glass windows. The quantum annealing-assisted active learning scheme may be generalized for the design of other complex materials.
Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, we propose a hybrid data‐driven strategy that integrates Bayesian Optimization (BO) and Gaussian Process Regression (GPR) to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe‐based thermoelectric materials. We collect data from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe‐based materials prepared using a simple high‐throughput ink mixing and blade coating method deliver a high power factor of 2100 μW/mK2, which is a 75% improvement from the baseline composite (nominal composition of Ag2Se1). The success of our study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.This article is protected by copyright. All rights reserved
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