| Abstract Scope |
Disordered multicomponent systems have been studied for the last two decades for their revolutionary properties. Resilient compositions can be stabilized by maximizing entropy (configurational/vibrational) of (near) equimolar mixtures. The search for new systems is mostly performed with trial-and-error techniques, as effective computational discovery is challenged by configurational immensity: the synthesizability of high-entropy ceramics is typically assessed using ideal entropy along with the formation enthalpies from density functional theory or with simplified descriptors or machine learning methods. Vibrations — even if they may have significant impact on phase stability — are drastically approximated to reduce the computational cost, or often avoided with the hope of them being negligible, due to the technical difficulties posed in calculating them for disordered systems. In this presentation I will address many of the problems in the discovery of disordered systems, offer some data-based effective solutions, and discuss the avenues opened by the latter, especially for plasmonic-hyperbolic applications. |