Materials design with target-oriented Bayesian optimization
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01704-4 |
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| _version_ | 1849334450517704704 |
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| author | Yuan Tian Tongtong Li Jianbo Pang Yumei Zhou Dezhen Xue Xiangdong Ding Turab Lookman |
| author_facet | Yuan Tian Tongtong Li Jianbo Pang Yumei Zhou Dezhen Xue Xiangdong Ding Turab Lookman |
| author_sort | Yuan Tian |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-32b1b5cfd26849f8b31b3fa9f8b532c1 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-32b1b5cfd26849f8b31b3fa9f8b532c12025-08-20T03:45:34ZengNature Portfolionpj Computational Materials2057-39602025-07-0111111110.1038/s41524-025-01704-4Materials design with target-oriented Bayesian optimizationYuan Tian0Tongtong Li1Jianbo Pang2Yumei Zhou3Dezhen Xue4Xiangdong Ding5Turab Lookman6Materials Genome Institute, Shanghai UniversitySchool of Materials Science and Engineering, Zhejiang Sci-Tech UniversityState Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong UniversityState Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong UniversityState Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong UniversityState Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong UniversityState Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong UniversityAbstract 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.https://doi.org/10.1038/s41524-025-01704-4 |
| spellingShingle | Yuan Tian Tongtong Li Jianbo Pang Yumei Zhou Dezhen Xue Xiangdong Ding Turab Lookman Materials design with target-oriented Bayesian optimization npj Computational Materials |
| title | Materials design with target-oriented Bayesian optimization |
| title_full | Materials design with target-oriented Bayesian optimization |
| title_fullStr | Materials design with target-oriented Bayesian optimization |
| title_full_unstemmed | Materials design with target-oriented Bayesian optimization |
| title_short | Materials design with target-oriented Bayesian optimization |
| title_sort | materials design with target oriented bayesian optimization |
| url | https://doi.org/10.1038/s41524-025-01704-4 |
| work_keys_str_mv | AT yuantian materialsdesignwithtargetorientedbayesianoptimization AT tongtongli materialsdesignwithtargetorientedbayesianoptimization AT jianbopang materialsdesignwithtargetorientedbayesianoptimization AT yumeizhou materialsdesignwithtargetorientedbayesianoptimization AT dezhenxue materialsdesignwithtargetorientedbayesianoptimization AT xiangdongding materialsdesignwithtargetorientedbayesianoptimization AT turablookman materialsdesignwithtargetorientedbayesianoptimization |