New Quasicrystal Approximant in the Sc–Pd System: From Topological Data Mining to the Bench

Intermetallics
contribute significantly to our current demand for
high-performance functional materials. However, understanding their
chemistry is still an open and debated topic, especially for complex
compounds such as approximants and quasicrystals. In this work, targeted
topological data mining succeeded in (i) selecting all known Mackay-type
approximants, (ii) uncovering the most important geometrical and chemical
factors involved in their formation, and (iii) guiding the experimental
work to obtain a new binary Sc–Pd 1/1 approximant for icosahedral
quasicrystals containing the desired cluster. Single-crystal X-ray
diffraction data analysis supplemented by electron density reconstruction
using the maximum entropy method, showed fine structural peculiarities,
that is, smeared electron densities in correspondence to some crystallographic
sites. These characteristics have been studied through a comprehensive
density functional theory modeling based on the combination of point
defects such as vacancies and substitutions. It was confirmed that
the structural disorder occurs in the shell enveloping the classical
Mackay cluster, so that the real structure can be viewed as an assemblage
of slightly different, locally ordered, four shell nanoclusters. Results
obtained here open up broader perspectives for machine learning with
the aim of designing novel materials in the fruitful field of quasicrystals
and their approximants. This might become an alternative and/or complementary
way to the electronic pseudogap tuning, often used before explorative
synthesis.