In an attempt to propose more general conditions for decoherence to occur, we study spectral and ergodic properties of unital, completely positive maps on not necessarily unital C *algebras, with a particular focus on gapped maps for which the transient portion of the arising dynamical system can be separated from the persistent one. After some general results, we first devote our attention to the abelian case by investigating the unital * -endomorphisms of, in general non-unital, C * -algebras, and their spectral structure. The finite dimensional case is also investigated in detail, and examples are provided of unital completely positive maps for which the persistent part of the associated dynamical system is equipped with the new product making it a C * -algebra, and the map under consideration restricts to a unital * -automorphism for this new C * -structure, thus generating a conservative dynamics on that persistent part.
Recent Machine Learning (ML) developments have opened new perspectives on accelerating the discovery of new materials. However, in the field of materials informatics, the performance of ML estimators is heavily limited by the nature of the available training datasets, which are often severely restricted and unbalanced. Among practitioners, it is usually taken for granted that more data corresponds to better performance. Here, we investigate whether different ML models for property predictions benefit from the aggregation of large databases into smaller repositories. To do this, we probe three different aggregation strategies prioritizing training size, element diversity, and composition diversity. For classic ML models, our results consistently show a reduction in performance under all the considered strategies. Deep Learning models show more robustness, but most changes are not significant. Furthermore, to assess whether this is a consequence of a distribution mismatch between datasets, we simulate the data acquisition process of a single dataset and compare a random selection with prioritizing chemical diversity. We observe that prioritizing composition diversity generally leads to a slower convergence toward better accuracy. Overall, our results suggest caution when merging different data sources and discourage a biased acquisition of novel chemistries when building a training dataset.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.