Estimation of inorganic crystal densities using gradient boosted trees
Density is a fundamental material property that can be used to determine a variety of other properties and the material’s feasibility for various applications, such as with energetic materials. However, current methods for determining density require significant resource investment, are computationa...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Materials |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2022.922566/full |
| _version_ | 1798025060300095488 |
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| author | Jesse Zhao |
| author_facet | Jesse Zhao |
| author_sort | Jesse Zhao |
| collection | DOAJ |
| description | Density is a fundamental material property that can be used to determine a variety of other properties and the material’s feasibility for various applications, such as with energetic materials. However, current methods for determining density require significant resource investment, are computationally expensive, or lack accuracy. We used the properties of roughly ∼15,000 inorganic crystals to develop a highly accurate machine learning algorithm that can predict density. Our algorithm takes in the desired crystal’s chemical formula and generates 249 predictors from online materials databases, which are fed into a gradient boosted trees model. It exhibits a strong predictive power with an R2 of ∼99%. |
| first_indexed | 2024-04-11T18:13:21Z |
| format | Article |
| id | doaj.art-1f43d6eacb6f49f797efb79a15de15a2 |
| institution | Directory Open Access Journal |
| issn | 2296-8016 |
| language | English |
| last_indexed | 2024-04-11T18:13:21Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj.art-1f43d6eacb6f49f797efb79a15de15a22022-12-22T04:10:02ZengFrontiers Media S.A.Frontiers in Materials2296-80162022-09-01910.3389/fmats.2022.922566922566Estimation of inorganic crystal densities using gradient boosted treesJesse ZhaoDensity is a fundamental material property that can be used to determine a variety of other properties and the material’s feasibility for various applications, such as with energetic materials. However, current methods for determining density require significant resource investment, are computationally expensive, or lack accuracy. We used the properties of roughly ∼15,000 inorganic crystals to develop a highly accurate machine learning algorithm that can predict density. Our algorithm takes in the desired crystal’s chemical formula and generates 249 predictors from online materials databases, which are fed into a gradient boosted trees model. It exhibits a strong predictive power with an R2 of ∼99%.https://www.frontiersin.org/articles/10.3389/fmats.2022.922566/fullcrystal densitycrystal density predictionsdensity predictioninorganic crystaldensity prediction model |
| spellingShingle | Jesse Zhao Estimation of inorganic crystal densities using gradient boosted trees Frontiers in Materials crystal density crystal density predictions density prediction inorganic crystal density prediction model |
| title | Estimation of inorganic crystal densities using gradient boosted trees |
| title_full | Estimation of inorganic crystal densities using gradient boosted trees |
| title_fullStr | Estimation of inorganic crystal densities using gradient boosted trees |
| title_full_unstemmed | Estimation of inorganic crystal densities using gradient boosted trees |
| title_short | Estimation of inorganic crystal densities using gradient boosted trees |
| title_sort | estimation of inorganic crystal densities using gradient boosted trees |
| topic | crystal density crystal density predictions density prediction inorganic crystal density prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2022.922566/full |
| work_keys_str_mv | AT jessezhao estimationofinorganiccrystaldensitiesusinggradientboostedtrees |