We present a set of Gupta potentials fitted against highest-level ab initio data for interactions of the alkaline earth metals: beryllium, magnesium, and calcium. Reference potential energy curves have been computed for both pure and mixed dimers with the coupled cluster method including corrections for basic set incompleteness and relativistic effects. To demonstrate their usability for the efficient description of high-dimensional complex energy landscapes, the obtained potentials have been used for the global optimization of 38- and 42-atom clusters. Both pure and mixed compositions (binary and ternary clusters) were investigated. Distinctive trends in the structure of the latter are discussed. 1. Introduction Metallic clusters have become over the years a subject of intense study, both theoretical as well as experimental [1]. Interest stems from the distinct properties they reveal when compared to the bulk phase and how these may change as a function of the cluster size. Different compositions (in binary, ternary, and higher mixtures) can also lead to new chemical and physical phenomena. Nanoalloys are a prime example of how both factors can be combined for material design and application in catalysis [2, 3]. The computational study of their structures is a challenging task for two interlacing reasons. On the one hand, the number of local minima is considered to scale exponentially with the cluster size, making the search for the global minimum NP-hard [4]. This property reflects back on all algorithms designed to explore the energy landscape of such systems. On the other hand, a suitable theoretical description of the interactions in play is required. It needs to be accurate enough to properly describe the energy landscape for a wide range of bonding patterns. It should also be amenable to computation, meaning that the computation of several hundred many-body interactions can be carried out in a sensible time frame. This is even more important since multiple thousands of these computations are required for a proper sampling of the energy landscape. One of the most successful approaches to the study of metallic clusters has been the combination of fitted potentials with global optimization algorithms [5–8]. The former are usually obtained by fitting experimental data or electronic structure results to an analytic expression. The brute force use of quantum mechanical methods is impractical due to the computational cost, particularly linked to its scaling relative to the system size. Even semiempirical methods may be too costly as the prefactors are
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