Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
Machine learning a defect’s effect A method for quickly predicting the dominant equilibrium atomic-level defects in a material is developed by researchers in the USA. Crystalline materials derive many of their attributes from the regular and symmetric arrangement of their atoms. Consequently, a miss...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2016
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a32c20d9819043b39e1bf57b270fa447 |
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| Sumario: | Machine learning a defect’s effect A method for quickly predicting the dominant equilibrium atomic-level defects in a material is developed by researchers in the USA. Crystalline materials derive many of their attributes from the regular and symmetric arrangement of their atoms. Consequently, a missing or an impurity atom can noticeably change these properties. A quantum physics method known as density functional theory calculations has proven to be a powerful method for predicting the influence of these so-called point defects. However, the brute-force application of these methods requires significant computing power, thus hindering its application in high throughput screening of thousands of materials for properties influenced by point defects. Bharat Medasani from the Lawrence Berkeley National Laboratory and co-workers combine machine learning with a few hundred density functional theory calculations to make this process much faster. They demonstrate the power of their approach by examining the properties of a family of binary intermetallic alloys. |
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