Colloidal molecules exhibit unique electronic,optical,and magnetic properties owing to their molecular-like configurations and coupling effects,making them promising building blocks for multifunctional materials.Howev...Colloidal molecules exhibit unique electronic,optical,and magnetic properties owing to their molecular-like configurations and coupling effects,making them promising building blocks for multifunctional materials.However,achieving precise and controllable assembly of isotropic nanoparticles with high yields remains a great challenge.In this study,we present a synergistic strategy that integrates molecular dynamics simulations with interpretable machine learning to develop a programmable assembly system based on block copolymers and DNA-functionalized nanoparticles.Our simulation results reveal that block copolymer modification facilitates stepwise control over surface phase separation and nanoparticle coassembly,thereby enhancing structural stability and efficiently suppressing disordered aggregation of atom-like nanoparticles.Furthermore,we demonstrated that precise,controllable,and programmable assembly of colloidal molecules can be achieved through rational DNA sequence design.SHapley Additive exPlanations(SHAP)analysis identified key structural descriptors that govern assembly outcomes and elucidated their underlying mechanistic roles.This work not only deepens the understanding of colloidal molecule assembly mechanisms but also lays a theoretical foundation for the rational design of functional colloidal architectures in nanomaterial science.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.92477118 and 22173045)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(No.KYCX25_0188)。
文摘Colloidal molecules exhibit unique electronic,optical,and magnetic properties owing to their molecular-like configurations and coupling effects,making them promising building blocks for multifunctional materials.However,achieving precise and controllable assembly of isotropic nanoparticles with high yields remains a great challenge.In this study,we present a synergistic strategy that integrates molecular dynamics simulations with interpretable machine learning to develop a programmable assembly system based on block copolymers and DNA-functionalized nanoparticles.Our simulation results reveal that block copolymer modification facilitates stepwise control over surface phase separation and nanoparticle coassembly,thereby enhancing structural stability and efficiently suppressing disordered aggregation of atom-like nanoparticles.Furthermore,we demonstrated that precise,controllable,and programmable assembly of colloidal molecules can be achieved through rational DNA sequence design.SHapley Additive exPlanations(SHAP)analysis identified key structural descriptors that govern assembly outcomes and elucidated their underlying mechanistic roles.This work not only deepens the understanding of colloidal molecule assembly mechanisms but also lays a theoretical foundation for the rational design of functional colloidal architectures in nanomaterial science.
