TY - JOUR
T1 - The Role of Transition Metals on CeO2 Supported for CO2 Adsorption by DFT and Machine Learning Analysis
AU - Mashhadimoslem, Hossein
AU - Karimi, Peyman
AU - Abdol, Mohammad Ali
AU - Zanganeh, Kourosh
AU - Shafeen, Ahmed
AU - AlHammadi, Ali A.
AU - Elkamel, Ali
N1 - Publisher Copyright:
© 2024 American Chemical Society
PY - 2024/6/26
Y1 - 2024/6/26
N2 - Catalyst design is a field where machine learning (ML) algorithms have found many useful applications using atomistic simulation data sets. Atomistic simulation of CO2 adsorption energy using several transition metals on ceria oxide (CeO2) catalysts is the subject of our research. The density functional theory (DFT) calculation was used, and its results were applied as a data set to train several ML algorithms. Gibbs free energy (ΔG) was simulated for all TMs such as Ni, Fe, Cu, Co, Mo, Ru, Rh, Pd, Ag, Pt, Zr, and Ti and used as the goal of CO2 adsorption prediction using ML algorithms. The XGBoost algorithm provided satisfactory predictions of ΔG using different transition metals and bond energy at different adsorption temperatures on the catalysts. The effective role of Cu, Pt, Fe, and Ni in all temperature ranges regarding the increase of CO2 adsorption energy was evident. The best CO2 adsorption efficiency will be achieved by Ni at temperatures below 300 K and Cu at temperatures over 300 K. Evaluation of new catalysts using prediction findings and correlations between complex DFT simulation parameters demonstrates the potential of ML as a powerful tool for industry development.
AB - Catalyst design is a field where machine learning (ML) algorithms have found many useful applications using atomistic simulation data sets. Atomistic simulation of CO2 adsorption energy using several transition metals on ceria oxide (CeO2) catalysts is the subject of our research. The density functional theory (DFT) calculation was used, and its results were applied as a data set to train several ML algorithms. Gibbs free energy (ΔG) was simulated for all TMs such as Ni, Fe, Cu, Co, Mo, Ru, Rh, Pd, Ag, Pt, Zr, and Ti and used as the goal of CO2 adsorption prediction using ML algorithms. The XGBoost algorithm provided satisfactory predictions of ΔG using different transition metals and bond energy at different adsorption temperatures on the catalysts. The effective role of Cu, Pt, Fe, and Ni in all temperature ranges regarding the increase of CO2 adsorption energy was evident. The best CO2 adsorption efficiency will be achieved by Ni at temperatures below 300 K and Cu at temperatures over 300 K. Evaluation of new catalysts using prediction findings and correlations between complex DFT simulation parameters demonstrates the potential of ML as a powerful tool for industry development.
UR - http://www.scopus.com/inward/record.url?scp=85196398603&partnerID=8YFLogxK
U2 - 10.1021/acs.iecr.4c01200
DO - 10.1021/acs.iecr.4c01200
M3 - Article
AN - SCOPUS:85196398603
SN - 0888-5885
VL - 63
SP - 11018
EP - 11029
JO - Industrial and Engineering Chemistry Research
JF - Industrial and Engineering Chemistry Research
IS - 25
ER -