First‐Principles Density Functional Theory and Machine Learning Technique for the Prediction of Water Adsorption Site on PtPd‐Based High‐Entropy‐Alloy Catalysts (Adv. Theory Simul. 4/2023). Issue 4 (12th April 2023)

• Record Type: Journal Article
• Authors: Rittiruam, Meena; Setasuban, Sorawee; Noppakhun, Jakapob; Saelee, Tinnakorn; Ektarawong, Annop; Aumnongpho, Nuttanon; Boonchuay, Suphawich; Khajondetchairit, Patcharaporn; Praserthdam, Supareak; Alling, Björn; Praserthdam, Piyasan
• Is Part Of: Advanced theory and simulations. Volume 6:Issue 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-04-12
• Subjects: Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202370008
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract : First‐Principles Density Functional Theory and Machine Learning Technique In article number 2200926, Supareak Praserthdam and co‐workers screen for water‐gas shift PtPd‐based high‐entropy alloys catalysts via Density Functional Theory and Machine Learning based on adsorption energy descriptor providing preferred H2 O adsorption site since the H2 O activation step plays an important role as the first step in such a reaction.