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<p>Materials design using Bayesian optimization (BO) typically focuses on optimizing materials properties by estimating the maxima/minima of unknown functions. However, materials often possess good properties at specific values or show effective response under certain conditions. We propose a target-oriented BO to efficiently suggest materials with target-specific properties. The method samples potential candidates by allowing their properties to approach the target value from either above or below, minimizing experimental iterations. We compare the performance of target-oriented BO with that of other BO methods on synthetic functions and materials databases. The average results from hundreds of repeated trials demonstrate target-oriented BO requires fewer experimental iterations to reach the same target, especially when the training dataset is small. We further employ the method to discover a thermally-responsive shape memory alloy Ti<sub>0.20</sub>Ni<sub>0.36</sub>Cu<sub>0.12</sub>Hf<sub>0.24</sub>Zr<sub>0.08</sub> with a transformation temperature difference of only 2.66 °C (0.58% of the range) from the target temperature in 3 experimental iterations. Our method provides a solution tailored for optimizing target-specific properties, facilitating the accelerated development of materials with predefined properties.</p>

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Materials design with target-oriented Bayesian optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月3日

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  • Yuan Tian,
  • Tongtong Li,
  • Jianbo Pang,
  • Yumei Zhou,
  • Dezhen Xue,
  • Xiangdong Ding,
  • Turab Lookman

摘要

Materials design using Bayesian optimization (BO) typically focuses on optimizing materials properties by estimating the maxima/minima of unknown functions. However, materials often possess good properties at specific values or show effective response under certain conditions. We propose a target-oriented BO to efficiently suggest materials with target-specific properties. The method samples potential candidates by allowing their properties to approach the target value from either above or below, minimizing experimental iterations. We compare the performance of target-oriented BO with that of other BO methods on synthetic functions and materials databases. The average results from hundreds of repeated trials demonstrate target-oriented BO requires fewer experimental iterations to reach the same target, especially when the training dataset is small. We further employ the method to discover a thermally-responsive shape memory alloy Ti0.20Ni0.36Cu0.12Hf0.24Zr0.08 with a transformation temperature difference of only 2.66 °C (0.58% of the range) from the target temperature in 3 experimental iterations. Our method provides a solution tailored for optimizing target-specific properties, facilitating the accelerated development of materials with predefined properties.