Gaussian process (GP) regression has been recently developed as an effective method in molecular geometry optimization. The prior mean function is one of the crucial parts of the GP. We design and validate two types of physically inspired prior mean functions: force-field-based priors and posterior-type priors.
View Article and Find Full Text PDFRecent work has demonstrated the promise of using machine-learned surrogates, in particular, Gaussian process (GP) surrogates, in reducing the number of electronic structure calculations (ESCs) needed to perform surrogate model based (SMB) geometry optimization. In this paper, we study geometry meta-optimization with GP surrogates where a SMB optimizer additionally learns from its past "experience" performing geometry optimization. To validate this idea, we start with the simplest setting where a geometry meta-optimizer learns from previous optimizations of the same molecule with different initial-guess geometries.
View Article and Find Full Text PDFBased on the conceptual model of multidimensional and hierarchical motivational climate the objective of this study was to test two models. One model (M1) of total mediation, testing the mediating mechanisms that explain why the motivational climate affects intention of continuity or dropout. Specifically, we test the mediating role of satisfaction/frustration of basic psychological needs and self-determined motivation, in the relationship between the players' perception of the empowering and disempowering climate created by the coach, and the intention of young soccer players to continue/dropout the sport practice.
View Article and Find Full Text PDFSternal agenesis as well as ectopia cordis are extremely rare congenital malformations. We here report a single case treated in the Department of Paediatric Surgery in Benin. The study involved a 3-year-old girl with congenital sternal agenesis associated with ectopia cordis; firstly, she underwent controlled healing.
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