Affiliation:
1. Department of Aerospace and Mechanical Engineering University of Notre Dame Notre Dame IN 46556 USA
2. Department of Chemical Engineering Texas Tech University Lubbock TX 79409 USA
3. Department of Chemical and Biomolecular Engineering University of Notre Dame Notre Dame IN 46556 USA
4. Department of Mechanical and Aerospace Engineering California State University Long Beach Long Beach CA 90840 USA
Abstract
AbstractOptimizing 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, a hybrid data‐driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe‐based thermoelectric materials. Data is collected 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 m−1 K−2, which is a 75% improvement from the baseline composite (nominal composition of Ag2Se1). The success of this 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.
Funder
U.S. Department of Energy
National Science Foundation
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science
Cited by
2 articles.
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