Prediction and synthesis of novel layered double hydroxide with desired basal spacing based on relevance vector machine
| Autor: | Li Kou, Pan Xiong, Xiaobo Ji, Xiuyun Zhai, Qing Zhang, Wencong Lu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Quantitative structure–activity relationship
Work (thermodynamics) Generic algorithms Mechanical Engineering Feature selection Nanotechnology 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences 0104 chemical sciences Power (physics) Relevance vector machine chemistry.chemical_compound chemistry Mechanics of Materials Approximation error Hydroxide General Materials Science 0210 nano-technology Biological system |
| Zdroj: | Materials Research Bulletin. 93:123-129 |
| ISSN: | 0025-5408 |
| DOI: | 10.1016/j.materresbull.2017.03.045 |
| Popis: | A Quantitative Structure Property Relationship (QSPR) model for the basal spacing of layered double hydroxide is developed in the present work by using generic algorithms feature selection, and relevance vector machine regression. The relative error of the developed model is 0.78% in cross-validation. Then, the QSPR model is applied to recommend a LDH with desired basal spacing for synthesis. The synthesized LDH meets the design requirement, thereby confirming the prediction power of the developed model. |
| Databáze: | OpenAIRE |
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