Nanometre-sized hybrid metal nanoparticles have many applications in different processes, including catalysis, nano-electronics, nanomedicine and biological imaging. Often it is important to know the detailed atomic structure of the particle in order to understand its functionality. The particles consist of a metal core and a protecting layer of molecules. High-resolution electron microscopes are able to produce 3D atomic structures of the metal core, but these instruments cannot detect the molecular layer that consists of light atoms such as carbon, nitrogen and oxygen.
The new algorithm published by the researchers in Jyväskylä helps to create accurate atomic models of the particles' total structure enabling simulations of the metal-molecule interface as well as of the surface of the molecular layer and its interactions with the environment. The algorithm can also rank the predicted atomic structural models based on how well the models reproduce measured properties of other particles of similar size and type.
"The basic idea behind our algorithm is very simple. Chemical bonds between atoms are always discrete, having well-defined bond angles and bond distances. Therefore, every nanoparticle structure known from experiments, where the positions of all atoms are resolved accurately, tells something essential about the chemistry of the metal-molecule interface. The interesting question regarding applications of artificial intelligence for structural predictions is: how many of these already known structures we need to know so that predictions for new, yet unknown particles become reliable? It looks like we only need a few dozen of known structures", commented the lead author of the article, Sami Malola, who works as a University Researcher at the Nanoscience Center of the University of Jyväskylä.
"In the next phase of this work we will build efficient atomic interaction models for hybrid metal nanoparticles by using machine learning methods. These models will allow us to investigate several interesting and important topics such as particle-particle reactions and the nanoparticles' ability to function as delivery vehicles for small drug molecules", stated Academy Professor Hannu Häkkinen, who led the study.
Hannu Häkkinen's collaborator, professor Tommi Kärkkäinen from the Faculty of Information Science in the University of Jyväskylä continued: "This is a significant step forward within the context of new interdisciplinary collaboration in our university. Applying artificial intelligence to challenging topics in nanoscience, such as structural predictions for new nanomaterials, will surely lead to new breakthroughs."
In addition to Sami Malola, Hannu Häkkinen and Tommi Kärkkäinen, the article was co-authored by University teacher Paavo Nieminen, PhD student Antti Pihlajamäki and postdoctoral researcher Joonas Hämäläinen. The work utilised supercomputer resources at the Finnish national supercomputing centre (CSC) and at the Barcelona Supercomputing Center (BSC), as a part of a PRACE - Partnership for Advanced Computing in Europe - project.
The paper titled " A method for structure prediction of metal-ligand interfaces of hybrid nanoparticles " has been published inNature Communications.