18,141 research outputs found
Complexity-entropy analysis at different levels of organization in written language
Written language is complex. A written text can be considered an attempt to
convey a meaningful message which ends up being constrained by language rules,
context dependence and highly redundant in its use of resources. Despite all
these constraints, unpredictability is an essential element of natural
language. Here we present the use of entropic measures to assert the balance
between predictability and surprise in written text. In short, it is possible
to measure innovation and context preservation in a document. It is shown that
this can also be done at the different levels of organization of a text. The
type of analysis presented is reasonably general, and can also be used to
analyze the same balance in other complex messages such as DNA, where a
hierarchy of organizational levels are known to exist
Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials.
Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors, which is known to be correlated to valley degeneracy in the electronic band structure. However, there are over 50 known semiconducting half-Heusler phases, and it is not clear how the chemical composition affects the electronic structure. While all the n-type electronic structures have their conduction band minimum at either the Γ- or X-point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the Γ-, L-, or W-point. Here, we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum. We do this by constructing an "orbital phase diagram" to cluster the variety of electronic structures expressed by these phases into groups, based on the atomic orbitals that contribute most to their valence bands. Then, with the aid of machine learning, we develop new chemical rules that predict the location of the valence band maximum in each of the phases. These rules can be used to engineer band structures with band convergence and high valley degeneracy
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