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Active learning for the design of polycrystalline textures using conditional normalizing flows.

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Title: Active learning for the design of polycrystalline textures using conditional normalizing flows.
Authors: Buzzy, Michael O.1 (AUTHOR), Montes de Oca Zapiain, David2 (AUTHOR), Generale, Adam P.3 (AUTHOR), Kalidindi, Surya R.1,3 (AUTHOR), Lim, Hojun1,2 (AUTHOR) hnlim@sandia.gov
Source: Acta Materialia. Jan2025, Vol. 284, pN.PAG-N.PAG. 1p.
Subjects: Cost control, Experimental design, Anisotropy, Curatorship, Cost
Abstract: Generative modeling has opened new avenues for solving previously intractable materials design problems. However, these new opportunities are accompanied by a drastic increase in the required amount of training data. This is in stark juxtaposition to the high expense and difficulty in curating such large materials datasets. In this work, we propose a novel framework for integrating generative models within an active learning loop. This enables the training of generative models with datasets significantly smaller than what has previously been demonstrated, providing a direct route for their application in data constrained environments. The functionality of this framework is then demonstrated by addressing the challenge of designing polycrystalline textures associated with target anisotropic mechanical properties. The developed protocol exhibited a cost reduction between 14 to 18 times over a randomly sampled experimental design. [Display omitted] [ABSTRACT FROM AUTHOR]
Copyright of Acta Materialia is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 181810463
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Generative modeling has opened new avenues for solving previously intractable materials design problems. However, these new opportunities are accompanied by a drastic increase in the required amount of training data. This is in stark juxtaposition to the high expense and difficulty in curating such large materials datasets. In this work, we propose a novel framework for integrating generative models within an active learning loop. This enables the training of generative models with datasets significantly smaller than what has previously been demonstrated, providing a direct route for their application in data constrained environments. The functionality of this framework is then demonstrated by addressing the challenge of designing polycrystalline textures associated with target anisotropic mechanical properties. The developed protocol exhibited a cost reduction between 14 to 18 times over a randomly sampled experimental design. [Display omitted] [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Acta Materialia is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.actamat.2024.120537
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      – Code: eng
        Text: English
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      – SubjectFull: Experimental design
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      – SubjectFull: Anisotropy
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      – SubjectFull: Cost
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              M: 01
              Text: Jan2025
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