Data-driven exploration of AB2X4 (X = O, S, Se, Te) spinel chemical space

Abstract

The discovery of materials has long been a fundamental building block for technological advancement, yet traditional trial-and-error methods are slow and costly to meet the growing demand for novel functionality, particularly in green energy technologies, energy storage, and electronics. In response to this challenge, high-throughput screening and data-driven workflows that combine computational simulations, machine learning models, and materials databases have emerged as powerful tools for accelerating materials discovery. Spinels (AB₂X₄) stand out as a versatile class of materials with applications ranging from energy storage to catalysis. However, their full compositional space remains largely unexplored. In this work, we present a data-driven framework to identify potentially synthesisable spinel compounds composed of the first 83 elements in the periodic table with oxygen (O²⁻) and three chalcogen anions (S²⁻, Se²⁻, Te²⁻). Over 30 000 charge-balanced and chemically plausible candidates, including inverse spinels, were sequentially filtered based on the stability, structural feasibility, and electronic properties criteria. Our workflow integrates materials databases, empirical heuristic rules, and machine learning predictions to efficiently reduce the candidate pool. As a result, 2303 novel spinel candidates were identified from this workflow, offering a diverse subset of target compounds for further investigation.