SVC_and_SISSO_files

Three new antimonides were discovered in the ternary K-Cd-Sb systems by a multifaceted approach that involves (i) employing a machine learning algorithm to pinpoint the compositional regions with low formation energy, (ii) rapid experimental compositional screening aided by hydride route; (iii) determination of optimal synthesis temperature from in-situ high-temperature powder X-ray diffraction data. In addition, we develop machine learning approaches to categorize and predict compositional regions in which a specific structural motif (layered, framework-, or cage-like) is more likely to form for the K-containing ternary intermetallic. These predictive models have been successfully tested on the recently discovered compounds, unlocking the potential of further optimization of targeted synthesis of the compounds of interest (with the specific structure details) from the favorable compositional regions (with low formation energy) using rapid compositional screening via hydride route