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We present a systematic performance analysis of first-principles basin-hopping (BH) runs, with the target to identify all low-energy isomers of small Si and Cu clusters described within density-functional theory. As representative and widely employed move classes we focus on single-particle and collective moves, in which one or all atoms in the cluster at once are displaced in a random direction by some prescribed move distance, respectively. The analysis provides detailed insights into the bottlenecks and governing factors for the sampling efficiency, as well as simple rules-of-thumb for near-optimum move settings, that are intriguingly independent of the distinctly different chemistry of Si and Cu. At corresponding settings, the observed performance of the BH algorithm employing two simple, general-purpose move classes is already very good, and for the small systems studied essentially limited by frequent revisits to a few dominant isomers.
The performance of density functional theory (DFT) approximations for predicting materials thermodynamics is typically assessed by comparing calculated and experimentally determined enthalpies of formation from elemental phases, {Delta}Hf. However, a
Associative memory Hamiltonian structure prediction potentials are not overly rugged, thereby suggesting their landscapes are like those of actual proteins. In the present contribution we show how basin-hopping global optimization can identify low-ly
The performance of perovskite solar cells recently exceeded 15% solar-to-electricity conversion efficiency for small-area devices. The fundamental properties of the active absorber layers, hybrid organic-inorganic perovskites formed from mixing metal
Chemisorbed molecules at a fuel cell electrode are a very sensitive probe of the surrounding electrochemical environment, and one that can be accurately monitored with different spectroscopic techniques. We develop a comprehensive electrochemical mod
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