Machine learning force field study of carboxylate ligands on the surface of zinc-blende CdSe quantum dots  

作　　者：Haibing Zhang Bichuan Cao Lei Huang Xiaogang Peng Linjun Wang 

机构地区：[1]Zhejiang Key Laboratory of Excited-State Energy Conversion and Energy Storage,Department of Chemistry,Zhejiang University,Hangzhou 310058,China

出　　处：《Nano Research》2024年第12期10685-10693,共9页纳米研究（英文版）

基　　金：support from the National Natural Science Foundation of China(No.22273082);the High-Performance Computing Center in Department of Chemistry,Zhejiang University.

摘　　要：In colloidal quantum dots(QDs),the geometries of surface ligands may play significant roles in tuning the electronic structure,optical spectra and exciton dynamics.We here propose an effective approach to build a diverse dataset of small QDs,based on which the machine learning force field(MLFF)can be obtained based on the DeePMD framework and the energy of each atom is expressed based on the local atomic structure.Using the obtained QD force field(QDFF),molecular dynamics simulation of large zinc-blende CdSe QDs passivated by carboxylate ligands is successfully carried out,and the complex surface structure is extensively studied.We find that bridging,tilted,chelating and claw geometries are the major geometries of carboxylate ligands in CdSe QDs,and the alkyl chain length of ligands plays a significant role.The Markov state model is utilized to reveal the detailed geometry transformation channels.Due to the high performance of QDFF,the present approach is promising for systematic studies of large QDs with different kinds of ligands that can be synthesized in experiment.

关 键 词：quantum dots ligand geometry molecular dynamics force field machine learning 

分 类 号：O62[理学—有机化学]