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...

Description complète

Détails bibliographiques
Publié dans:Langmuir : the ACS journal of surfaces and colloids. - 1985. - 39(2023), 14 vom: 11. Apr., Seite 4984-4992
Auteur principal: Yoon, Sung-Ho (Auteur)
Autres auteurs: Jeon, Jun-Hyeok, Cho, Seung-Beom, Nacpil, Edric John Cruz, Jeon, Il, Choi, Jae-Boong, Kim, Hyeongkeun
Format: Article en ligne
Langue:English
Publié: 2023
Accès à la collection:Langmuir : the ACS journal of surfaces and colloids
Sujets:Journal Article
LEADER 01000caa a22002652c 4500
001 NLM354526790
003 DE-627
005 20250304134320.0
007 cr uuu---uuuuu
008 231226s2023 xx |||||o 00| ||eng c
024 7 |a 10.1021/acs.langmuir.2c03465  |2 doi 
028 5 2 |a pubmed25n1181.xml 
035 |a (DE-627)NLM354526790 
035 |a (NLM)36947443 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Yoon, Sung-Ho  |e verfasserin  |4 aut 
245 1 0 |a Extreme Gradient Boosting to Predict Atomic Layer Deposition for Platinum Nano-Film Coating 
264 1 |c 2023 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 11.04.2023 
500 |a Date Revised 15.04.2023 
500 |a published: Print-Electronic 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a 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 
650 4 |a Journal Article 
700 1 |a Jeon, Jun-Hyeok  |e verfasserin  |4 aut 
700 1 |a Cho, Seung-Beom  |e verfasserin  |4 aut 
700 1 |a Nacpil, Edric John Cruz  |e verfasserin  |4 aut 
700 1 |a Jeon, Il  |e verfasserin  |4 aut 
700 1 |a Choi, Jae-Boong  |e verfasserin  |4 aut 
700 1 |a Kim, Hyeongkeun  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t Langmuir : the ACS journal of surfaces and colloids  |d 1985  |g 39(2023), 14 vom: 11. Apr., Seite 4984-4992  |w (DE-627)NLM098181009  |x 1520-5827  |7 nnas 
773 1 8 |g volume:39  |g year:2023  |g number:14  |g day:11  |g month:04  |g pages:4984-4992 
856 4 0 |u http://dx.doi.org/10.1021/acs.langmuir.2c03465  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_22 
912 |a GBV_ILN_350 
912 |a GBV_ILN_721 
951 |a AR 
952 |d 39  |j 2023  |e 14  |b 11  |c 04  |h 4984-4992