| Project Detail |
Metal nanoclusters (MNC) are atomically precise metal nanoparticles with definite mass, structure, and chemical composition. Their metal core of 1-3 nm in diameter exhibits quantized electronic structure, and they are chemically stabilized by a molecular surface layer which is modifiable for functionalization and optimized biocompatibility, making them promising materials for novel applications in bioimaging, biosensing and nanomedicine as fluorescent markers, sensors and targeting drug carriers, paving way to personalized medicine and therapeutics. Their ultrasmall size makes them amenable to atom-scale modeling which may greatly help experimental efforts to design their properties for applications. However, consolidated simulation strategies capable of dealing with phenomena over a wide range of dynamical processes at the nanocluster – biomolecule interfaces are missing. To address this need, DYNANOINT will develop new multiscale simulation strategies, assessing critically graph theory and machine learning (ML) methods as potential accelerators for discovery of structure-function relationships combined with traditional electronic structure methods and force-field based dynamical simulations. The methodology is applied to (i) weak chemical interactions between MNCs and proteins, (ii) electronic excitations and charge-transfer interactions at the MNC – environment interface, and (iii) chiral MNC – environment interfaces. PI’s extensive collaboration network to three key experiments around the world ensures efficient spread of impact of this theoretical-computational project to real-life applications. The project has a broad impact on computational nanoscience since the developed open-source software will give new tools to study structure-function relations in low-dimensional, low-symmetry nanostructures for which combination of robust, transferable, and interpretable ML models with traditional simulations methods poses a significant contemporary challenge. |
