Machine learning predictions of high-Curie-temperature materials Article Swipe
Joshua F. Belot
,
Valentin Taufour
,
Stefano Sanvito
,
Gus L. W. Hart
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1063/5.0156377
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1063/5.0156377
Technologies that function at room temperature often require magnets with a high Curie temperature, TC, and can be improved with better materials. Discovering magnetic materials with a substantial TC is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known datasets of experimental Curie temperatures, we develop machine-learning models to make rapid TC predictions solely based on the chemical composition of a material. We train a random-forest model and a k-NN one and predict on an initial dataset of over 2500 materials and then validate the model on a new dataset containing over 3000 entries. The accuracy is compared for multiple compounds' representations (“descriptors”) and regression approaches. A random-forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. A random-forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-TC materials and under-predict high-TC materials. For exhaustive searches to find new high-TC materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.
Related Topics
Concepts
Curie temperature
Random forest
Ternary operation
Curse of dimensionality
Machine learning
Marie curie
Dimensionality reduction
Curie
Materials science
Regression
Artificial intelligence
Computer science
Thermodynamics
Condensed matter physics
Mathematics
Physics
Statistics
Ferromagnetism
Economic policy
Business
European union
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0156377
- OA Status
- green
- Cited By
- 16
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385246456
All OpenAlex metadata
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https://openalex.org/W4385246456Canonical identifier for this work in OpenAlex
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https://doi.org/10.1063/5.0156377Digital Object Identifier
- Title
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Machine learning predictions of high-Curie-temperature materialsWork 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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2023-07-24Full publication date if available
- Authors
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Joshua F. Belot, Valentin Taufour, Stefano Sanvito, Gus L. W. HartList of authors in order
- Landing page
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https://doi.org/10.1063/5.0156377Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.osti.gov/biblio/2423808Direct OA link when available
- Concepts
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Curie temperature, Random forest, Ternary operation, Curse of dimensionality, Machine learning, Marie curie, Dimensionality reduction, Curie, Materials science, Regression, Artificial intelligence, Computer science, Thermodynamics, Condensed matter physics, Mathematics, Physics, Statistics, Ferromagnetism, Economic policy, Business, European union, Programming languageTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2025: 10, 2024: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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