Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural network Article Swipe
Wissam A. Saidi
,
Waseem Shadid
,
Ivano E. Castelli
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-020-0307-8
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-020-0307-8
The development of statistical tools based on machine learning (ML) and deep networks is actively sought for materials design problems. While structure-property relationships can be accurately determined using quantum mechanical methods, these first-principles calculations are computationally demanding, limiting their use in screening a large set of candidate structures. Herein, we use convolutional neural networks to develop a predictive model for the electronic properties of metal halide perovskites (MHPs) that have a billions-range materials design space. We show that a well-designed hierarchical ML approach has a higher fidelity in predicting properties of the MHPs compared to straight-forward methods. In this architecture, each neural network element has a designated role in the estimation process from predicting complex features of the perovskites such as lattice constant and octahedral till angle to narrowing down possible ranges for the values of interest. Using the hierarchical ML scheme, the obtained root-mean-square errors for the lattice constants, octahedral angle and bandgap for the MHPs are 0.01 Å, 5°, and 0.02 eV, respectively. Our study underscores the importance of a careful network design and a hierarchical approach to alleviate issues associated with imbalanced dataset distributions, which is invariably common in materials datasets.
Related Topics
Concepts
Convolutional neural network
Artificial neural network
Octahedron
Computer science
Lattice constant
Halide
Band gap
Lattice (music)
Support vector machine
Artificial intelligence
Algorithm
Materials science
Deep learning
Machine learning
Physics
Crystal structure
Chemistry
Optics
Optoelectronics
Crystallography
Inorganic chemistry
Diffraction
Acoustics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41524-020-0307-8
- https://www.nature.com/articles/s41524-020-0307-8.pdf
- OA Status
- gold
- Cited By
- 145
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3016987575
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3016987575Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41524-020-0307-8Digital Object Identifier
- Title
-
Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural networkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-14Full publication date if available
- Authors
-
Wissam A. Saidi, Waseem Shadid, Ivano E. CastelliList of authors in order
- Landing page
-
https://doi.org/10.1038/s41524-020-0307-8Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41524-020-0307-8.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41524-020-0307-8.pdfDirect OA link when available
- Concepts
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Convolutional neural network, Artificial neural network, Octahedron, Computer science, Lattice constant, Halide, Band gap, Lattice (music), Support vector machine, Artificial intelligence, Algorithm, Materials science, Deep learning, Machine learning, Physics, Crystal structure, Chemistry, Optics, Optoelectronics, Crystallography, Inorganic chemistry, Diffraction, AcousticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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145Total citation count in OpenAlex
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2025: 25, 2024: 25, 2023: 31, 2022: 30, 2021: 26Per-year citation counts (last 5 years)
- References (count)
-
53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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