The secret ingredient of most catalysts used in the chemical industries, but also in objects of daily use, such as the catalytic converters in our cars, are tiny metallic particles with diameters in the nanometer range. We take advantage of the fact that the properties of these nanoparticles vary strongly with their size, their shape and the type of metals involved. This allows, at least in principle, also the adjustment of catalytic properties.
However, little is known about the behavior of materials in this size regime. The main reason for this lack of information is the still very large number of atoms forming a metal 'cluster', which makes an exact description of such a many-body quantum system impossible. Fortunately, very good results can be achieved by the application of electronic structure methods such as density functional theory. However, the computational effort of the latter technique grows approximately with the fifth power of the system size. One way to deal with this problem is the combination of a highly accurate method for the evaluation of energies for a subset of particles of manageable size with a clever computer program which is able to learn basic features from the data provided. If the training data set is large enough, a reliable prediction of energies for related, but unknown systems can be given in a fraction of the original computational time. Particularly interesting for this task are neural networks, a machine-learning concept inspired by nature, which is based on an abstraction of the central nervous system.
This project is dedicated to the development of such a neural network for the simulation of nanoparticles consisting of about 10 to 1000 metal atoms. We focus on the noble metals silver and gold, which are known to be catalytically highly active at this size, and investigate the structures of pure and mixed-metallic clusters. At a later stage, the force fields derived will be used to simulate the adsorption of selected gas molecules. Our long-term goal is to gain new insights in the effects of particle size, shape, inner structure and metallic ratio on the catalytic properties of small bimetallic clusters.