We introduce a computational method for global optimization of structure and ordering in atomic systems. The method relies on interpolation between chemical elements, which is incorporated in a machine-learning structural fingerprint. The method is based on Bayesian optimization with Gaussian processes and is applied to the global optimization of Au-Cu bulk systems, Cu-Ni surfaces with CO adsorption, and Cu-Ni clusters. The method consistently identifies low-energy structures, which are likely to be the global minima of the energy. For the investigated systems with 23-66 atoms, the number of required energy and force calculations is in the range 3-75.
Deposition of La0.85Sr0.15MnO3 (LSM) films from suspensions using a magnetic field was found to be a cheap and quick technique. Ninety weight percent of the particles present in the suspensions were deposited within the first minute of the deposition, and the thickness of the film varied linearly with the concentration of the suspension. Deposition phenomena were explained by modeling the magnetic flux in the deposition cell. Particles aligned with the flux lines, forming chains of LSM particles that, upon sintering, resulted in the formation of porous films with long chains of LSM grains.
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