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, 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−1K−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.
This content will become publicly available on May 11, 2024
The development of new materials and their compositional and microstructural optimization are essential in regard to next-generation technologies such as clean energy and environmental sustainability. However, materials discovery and optimization have been a frustratingly slow process. The Edisonian trial-and-error process is time consuming and resource inefficient, particularly when contrasted with vast materials design spaces1. Whereas traditional combinatorial deposition methods can generate material libraries2,3, these suffer from limited material options and inability to leverage major breakthroughs in nanomaterial synthesis. Here we report a high-throughput combinatorial printing method capable of fabricating materials with compositional gradients at microscale spatial resolution. In situ mixing and printing in the aerosol phase allows instantaneous tuning of the mixing ratio of a broad range of materials on the fly, which is an important feature unobtainable in conventional multimaterials printing using feedstocks in liquid–liquid or solid–solid phases4–6. We demonstrate a variety of high-throughput printing strategies and applications in combinatorial doping, functional grading and chemical reaction, enabling materials exploration of doped chalcogenides and compositionally graded materials with gradient properties. The ability to combine the top-down design freedom of additive manufacturing with bottom-up control over local material compositions promises the development of compositionally complex materials inaccessible via conventional manufacturing approaches.
more » « less- Award ID(s):
- 1747685
- NSF-PAR ID:
- 10466610
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- Nature
- Volume:
- 617
- Issue:
- 7960
- ISSN:
- 0028-0836
- Page Range / eLocation ID:
- 292 to 298
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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