Extreme Gradient Boosting to Predict Atomic Layer Deposition for Platinum Nano-Film Coating
Extreme gradient boosting (XGBoost) is an artificial intelligence algorithm capable of high accuracy and low inference time. The current study applies this XGBoost to the production of platinum nano-film coating through atomic layer deposition (ALD). In order to generate a database for model develop...
| Veröffentlicht in: | Langmuir : the ACS journal of surfaces and colloids. - 1992. - 39(2023), 14 vom: 11. Apr., Seite 4984-4992 |
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| Weitere Verfasser: | , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | Langmuir : the ACS journal of surfaces and colloids |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Extreme gradient boosting (XGBoost) is an artificial intelligence algorithm capable of high accuracy and low inference time. The current study applies this XGBoost to the production of platinum nano-film coating through atomic layer deposition (ALD). In order to generate a database for model development, platinum is coated on α-Al2O3 using a rotary-type ALD equipment. The process is controlled by four parameters: process temperature, stop valve time, precursor pulse time, and reactant pulse time. A total of 625 samples according to different process conditions are obtained. The ALD coating index is used as the Al/Pt component ratio through ICP-AES analysis during postprocessing. The four process parameters serve as the input data and produces the Al/Pt component ratio as the output data. The postprocessed data set is randomly divided into 500 training samples and 125 test samples. XGBoost demonstrates 99.9% accuracy and a coefficient of determination of 0.99. The inference time is lower than that of random forest regression, in addition to a higher prediction safety than that of the light gradient boosting machine |
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| Beschreibung: | Date Completed 11.04.2023 Date Revised 15.04.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1520-5827 |
| DOI: | 10.1021/acs.langmuir.2c03465 |