End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design Article Swipe
Han Liu
,
Yuhan Liu
,
Kevin Li
,
Zhangji Zhao
,
Samuel S. Schoenholz
,
Ekin D. Cubuk
,
Puneet Gupta
,
Mathieu Bauchy
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-023-01080-x
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-023-01080-x
Numerical simulations have revolutionized material design. However, although simulations excel at mapping an input material to its output property, their direct application to inverse design has traditionally been limited by their high computing cost and lack of differentiability. Here, taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm, we introduce a computational inverse design framework that addresses these challenges, by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end differentiation. Thanks to its differentiability, the simulation is used to directly train a deep generative model, which outputs an optimal porous matrix based on an arbitrary input sorption isotherm curve. Importantly, this inverse design pipeline leverages the power of tensor processing units (TPU)—an emerging family of dedicated chips, which, although they are specialized in deep learning, are flexible enough for intensive scientific simulations. This approach holds promise to accelerate inverse materials design.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41524-023-01080-x
- https://www.nature.com/articles/s41524-023-01080-x.pdf
- OA Status
- gold
- Cited By
- 9
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3161424953
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Raw OpenAlex JSON
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https://openalex.org/W3161424953Canonical identifier for this work in OpenAlex
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https://doi.org/10.1038/s41524-023-01080-xDigital Object Identifier
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End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse designWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-13Full publication date if available
- Authors
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Han Liu, Yuhan Liu, Kevin Li, Zhangji Zhao, Samuel S. Schoenholz, Ekin D. Cubuk, Puneet Gupta, Mathieu BauchyList of authors in order
- Landing page
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https://doi.org/10.1038/s41524-023-01080-xPublisher landing page
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https://www.nature.com/articles/s41524-023-01080-x.pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41524-023-01080-x.pdfDirect OA link when available
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Differentiable function, Inverse, Computer science, Computational science, Pipeline (software), Mathematical optimization, Mathematics, Mathematical analysis, Geometry, Programming languageTop concepts (fields/topics) attached by OpenAlex
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2025: 5, 2024: 2, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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