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We present a method for accelerating the global structure optimization of atomic compounds. The method is demonstrated to speed up the finding of the anatase TiO2(001)-(1 × 4) surface reconstruction within a density functional tight-binding theory framework using an evolutionary algorithm. As a key element of the method, we use unsupervised machine learning techniques to categorize atoms present in a diverse set of partially disordered surface structures into clusters of atoms having similar local atomic environments. Analysis of more than 1000 different structures shows that the total energy of the structures correlates with the summed distances of the atomic environments to their respective cluster centers in feature space, where the sum runs over all atoms in each structure. Our method is formulated as a gradient based minimization of this summed cluster distance for a given structure and alternates with a standard gradient based energy minimization. While the latter minimization ensures local relaxation within a given energy basin, the former enables escapes from meta-stable basins and hence increases the overall performance of the global optimization. [ABSTRACT FROM AUTHOR]

ABSTRACT We present the detection and analysis of line index variations in the pulsating sdB star PG 1605+072. We have found a strong dependence of line index amplitude on Balmer line order, with high-order Balmer line amplitudes up to 10 times larger than Hβ. Using a simple model, we have found that the line index may not only be dependent on temperature, as is usually assumed for oscillating stars, but also on surface gravity. This information will provide another set of observables that can be used for mode identification of sdBs. [ABSTRACT FROM AUTHOR]