Towards chemical space exploration for functional nanostructured systems

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Nanostructured and (supra-) molecular materials offer intriguing solutions for applications such as catalysis, biomedicine, thermoelectrics, batteries, and sensing. Our overall objective is to understand and design structural models of functional nanostructured systems, with two representative design goals of ferromagnetic coupling over large distances and narrow conductance distributions in fluctuating molecular junctions. For this purpose, we will develop and implement a reliable and efficient theoretical methodology for evaluating a) exchange spin coupling constants in dinuclear transition metal complexes and b) conductances for fluctuating molecular junctions. For efficiency, we will rely on machine learning, and for reliability, we will employ methods that provide intrinsic error measures. Combined with active learning, this with help us lay the groundwork for exploring the chemical space of functional nanostructured systems. Furthermore, our goal is to gain as much insight as possible from our machine learning models. This will allow us not only to understand structure-property relationships but also robust extrapolation and to understand why a certain machine learning model is successful or not. As a long-term vision, extensions towards large systems such as molecular layers on surfaces or metal-organic frameworks can allow for predictions relevant for technological applications in, e.g., catalysis, sensing, gas separation, and spintronics.