摘要
Bimetallic nanoparticles(AmBn)usually exhibit rich catalytic chemistry and have drawn tremendous attention in heterogeneous catalysis.However,challenged by the huge configuration space,the understanding toward their composition and distribution of A/B element is known little at the atomic level,which hinders the rational synthesis.Herein,we develop an on-the-fly training strategy combing the machine learning model(SchNet)with the genetic algorithm(GA)search technique,which achieve the fast and accurate energy prediction of complex bimetallic clusters at the DFT level.Taking the 38-atom PtmAu38-mnanoparticle as example,the element distribution identification problem and the stability trend as a function of Pt/Au composition is quantitatively re solved.Specifically,results show that on the Pt-rich cluster Au atoms prefer to occupy the low-coordinated surface corner sites and form patch-like surface segregation patte rns,while for the Au-rich ones Pt atoms tend to site in the co re region and form the co re-shell(Pt@Au)configuration.The thermodynamically most stable PtmAu38-mcluster is Pt6 Au32,with all the core-region sites occupied by Pt,rationalized by the stronger Pt-Pt bond in comparison with Pt-Au and Au-Au bonds.This work exemplifies the potent application of rapid global sea rch enabled by machine learning in exploring the high-dimensional configuration space of bimetallic nanocatalysts.
基金
supported by National Key R&D Program of China(No.2018YFA0208602)
NSFC(Nos.21622305,21873028,21703067)
National Ten Thousand Talent Program for Young Top-notch Talents in China
Shanghai ShuGuang project(No.17SG30)。
