Universal fragment descriptors for predicting properties of inorganic crystals
Machine learning methods can be useful for materials discovery; however certain properties remain difficult to predict. Here, the authors present a universal machine learning approach for modelling the properties of inorganic crystals, which is validated for eight electronic and thermomechanical pro...
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| Main Authors: | , , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2017
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| Subjects: | |
| Online Access: | https://doaj.org/article/e51aabf9bad84fe6a8a7bf233cac04c5 |
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| Summary: | Machine learning methods can be useful for materials discovery; however certain properties remain difficult to predict. Here, the authors present a universal machine learning approach for modelling the properties of inorganic crystals, which is validated for eight electronic and thermomechanical properties. |
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