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Much of our understanding of complex structures is based on simplification: for example, metal-organic frameworks are often discussed in the context of nodes and linkers, allowing for a qualitative comparison with simpler inorganic structures. Here we show how such an understanding can be obtained in a systematic and quantitative framework, by combining atom-density based similarity (kernel) functions and unsupervised machine learning with the long-standing idea of coarse-graining atomic structure. We demonstrate how the latter enables a comparison of vastly different chemical systems, and use it to create a unified, two-dimensional structure map of experimentally known tetrahedral AB2 networks - including clathrate hydrates, zeolitic imidazolate frameworks (ZIFs), and diverse inorganic phases. The structural relationships that emerge can then be linked to microscopic properties of interest, which we exemplify for structural heterogeneity and tetrahedral density.
Here we demonstrate that significant progress in this area may be achieved by introducing structural elements that form hydrogen bonds with environment. Considering several examples of hybrid framework materials with different structural ordering con
A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that machine learning can be used to learn such relationships, allowing the st
We consider the application of fluctuation relations to the dynamics of coarse-grained systems, as might arise in a hypothetical experiment in which a system is monitored with a low-resolution measuring apparatus. We analyze a stochastic, Markovian j
One-dimensional inorganic nanotubes hold promise for technological applications due to their distinct physical/chemical properties, but so far advancements have been hampered by difficulties in producing single-wall nanotubes with a well-defined radi
Suppose we have a pair of information channels, $kappa_{1},kappa_{2}$, with a common input. The Blackwell order is a partial order over channels that compares $kappa_{1}$ and $kappa_{2}$ by the maximal expected utility an agent can obtain when decisi