Machine learning assisted design of high entropy alloys with desired property
We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value i...
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| Published in: | Acta materialia Vol. 170; no. C; pp. 109 - 117 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Elsevier Ltd
15.05.2019
Elsevier |
| Subjects: | |
| ISSN: | 1359-6454, 1873-2453 |
| Online Access: | Get full text |
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| Abstract | We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. We find that a strategy using both the compositions and descriptors based on a knowledge of the properties of HEAs, outperforms that merely based on the compositions alone. This strategy offers a recipe to rapidly optimize multi-component systems, such as bulk metallic glasses and superalloys, towards desired properties.
In the present study, we proposed a data-driven approach combining machine learning, experimental design and feedback from experiment to accelerate the search for multi-component alloys with target properties. We demonstrated the efficiency of our approach by rapidly obtaining several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. In Iteration Loop I, a machine learning surrogate model is trained to learn the property-composition, relationship, yi=f(ci), with associated uncertainties. The model is applied to the vast unexplored space and a utility function is employed to recommend the most informative candidate for the next experiment, which balances the exploitation and exploration. Feedback from experimental synthesis and characterization allows the subsequent iterative improvement of the surrogate model. Iteration Loop II is essentially same as Iteration Loop I, except that a features pool was introduced to the Iteration Loop I and a surrogate model is trained from composition (ci) and the preselected physical features (pi), yi=f(ci,pi). We found that the approach using both the composition and the descriptors based on domain knowledge can more effectively accelerate material optimization compared to the approach using only the compositions. [Display omitted] |
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| AbstractList | We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. We find that a strategy using both the compositions and descriptors based on a knowledge of the properties of HEAs, outperforms that merely based on the compositions alone. This strategy offers a recipe to rapidly optimize multi-component systems, such as bulk metallic glasses and superalloys, towards desired properties.
In the present study, we proposed a data-driven approach combining machine learning, experimental design and feedback from experiment to accelerate the search for multi-component alloys with target properties. We demonstrated the efficiency of our approach by rapidly obtaining several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. In Iteration Loop I, a machine learning surrogate model is trained to learn the property-composition, relationship, yi=f(ci), with associated uncertainties. The model is applied to the vast unexplored space and a utility function is employed to recommend the most informative candidate for the next experiment, which balances the exploitation and exploration. Feedback from experimental synthesis and characterization allows the subsequent iterative improvement of the surrogate model. Iteration Loop II is essentially same as Iteration Loop I, except that a features pool was introduced to the Iteration Loop I and a surrogate model is trained from composition (ci) and the preselected physical features (pi), yi=f(ci,pi). We found that the approach using both the composition and the descriptors based on domain knowledge can more effectively accelerate material optimization compared to the approach using only the compositions. [Display omitted] |
| Author | Antonov, Stoichko Wen, Cheng Dai, Lanhong Su, Yanjing Bai, Yang Wang, Changxin Zhang, Yan Xue, Dezhen Lookman, Turab |
| Author_xml | – sequence: 1 givenname: Cheng surname: Wen fullname: Wen, Cheng organization: Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, China – sequence: 2 givenname: Yan surname: Zhang fullname: Zhang, Yan organization: Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, China – sequence: 3 givenname: Changxin surname: Wang fullname: Wang, Changxin organization: Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, China – sequence: 4 givenname: Dezhen orcidid: 0000-0001-6132-1236 surname: Xue fullname: Xue, Dezhen email: xuedezhen@xjtu.edu.cn organization: State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China – sequence: 5 givenname: Yang orcidid: 0000-0002-6917-256X surname: Bai fullname: Bai, Yang organization: Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, China – sequence: 6 givenname: Stoichko orcidid: 0000-0001-8886-2040 surname: Antonov fullname: Antonov, Stoichko organization: Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, China – sequence: 7 givenname: Lanhong surname: Dai fullname: Dai, Lanhong organization: Laboratory for Nonlinear Mechanics of Continuous Media (LNM), Institute of Mechanics, Chinese Academy of Sciences, Beijing, 100080, China – sequence: 8 givenname: Turab surname: Lookman fullname: Lookman, Turab organization: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA – sequence: 9 givenname: Yanjing surname: Su fullname: Su, Yanjing email: yjsu@ustb.edu.cn organization: Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, China |
| BackLink | https://www.osti.gov/biblio/1637031$$D View this record in Osti.gov |
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| PublicationCentury | 2000 |
| PublicationDate | 2019-05-15 |
| PublicationDateYYYYMMDD | 2019-05-15 |
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| PublicationTitle | Acta materialia |
| PublicationYear | 2019 |
| Publisher | Elsevier Ltd Elsevier |
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