[Published online Journal of Computer Chemistry, Japan Vol.23, 19-23, by J-STAGE]
<Title:> Machine Learning in Catalysis: Analysis and Prediction of CO Adsorption on Multi-elemental Nanoparticle using Metal Coordination-based Regression Model
<Author(s):> Susan Menez ASPERA, Gerardo Valadez HUERTA, Yusuke NANBA, Kaoru HISAMA, Michihisa KOYAMA
<Corresponding author E-Mill:> aspera_susan(at)shinshu-u.ac.jp
<Abstract:> Information about molecular adsorption strength is important in every catalytic reaction. The ability to compare and determine relevant molecular active sites of interaction is necessary for fast screening of potential catalysts specially in a vast spectrum of probable candidates. In this study, we used the metal-coordination of the adsorption sites as a descriptor of the adsorption energy of CO on the PtRuIr ternary alloy nanoparticle. Using multiple regression model, we are able to predict the adsorption energy and specify some important descriptors that controls the strength of CO adsorption energy. This will enable a fast prediction of CO adsorption energy on PtRuIr nanoparticles with varying compositions and possible different morphologies using only the information of the structure of the catalyst. And open up the possibility of predicting adsorption interaction of other combinations of alloys with higher number of metallic compositions for fast screening of appropriate molecule-surface interaction.
<Keywords:> multi-elemental nanoparticle alloy, machine learning, generalized coordination number, CO adsorption, PtRuIr ternary alloy, multiple regression
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/1/23_2024-0006/_article/-char/ja/
<Title:> Machine Learning in Catalysis: Analysis and Prediction of CO Adsorption on Multi-elemental Nanoparticle using Metal Coordination-based Regression Model
<Author(s):> Susan Menez ASPERA, Gerardo Valadez HUERTA, Yusuke NANBA, Kaoru HISAMA, Michihisa KOYAMA
<Corresponding author E-Mill:> aspera_susan(at)shinshu-u.ac.jp
<Abstract:> Information about molecular adsorption strength is important in every catalytic reaction. The ability to compare and determine relevant molecular active sites of interaction is necessary for fast screening of potential catalysts specially in a vast spectrum of probable candidates. In this study, we used the metal-coordination of the adsorption sites as a descriptor of the adsorption energy of CO on the PtRuIr ternary alloy nanoparticle. Using multiple regression model, we are able to predict the adsorption energy and specify some important descriptors that controls the strength of CO adsorption energy. This will enable a fast prediction of CO adsorption energy on PtRuIr nanoparticles with varying compositions and possible different morphologies using only the information of the structure of the catalyst. And open up the possibility of predicting adsorption interaction of other combinations of alloys with higher number of metallic compositions for fast screening of appropriate molecule-surface interaction.
<Keywords:> multi-elemental nanoparticle alloy, machine learning, generalized coordination number, CO adsorption, PtRuIr ternary alloy, multiple regression
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/1/23_2024-0006/_article/-char/ja/
