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Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting

Engineering and Technology

Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting

S. V. Oh, S. Yoo, et al.

This study, conducted by Seung-Hyun Victor Oh, Su-Hyun Yoo, and Woosun Jang, introduces a novel machine learning technique that efficiently navigates the design space of ZnTe-based high-entropy alloys for water splitting, leveraging limited data to predict crucial band edge positions for enhanced photocatalytic efficiency.

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Playback language: English
Abstract
This study demonstrates a machine learning approach using a small database to efficiently explore the design space of ZnTe-based high-entropy alloys for water splitting. The method combines the Sure Independence Screening and Sparsifying Operator (SISSO) with an agreement method (α-method) to predict band edge positions, crucial for photocatalytic efficiency. The approach effectively identifies optimal alloy compositions for water splitting, even with limited data, and proposes design routes for tuning material properties.
Publisher
npj Computational Materials
Published On
Jul 30, 2024
Authors
Seung-Hyun Victor Oh, Su-Hyun Yoo, Woosun Jang
Tags
machine learning
ZnTe-based alloys
water splitting
photocatalytic efficiency
high-entropy alloys
design space exploration

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