Data-Mining Element Charges in Inorganic Materials

TL;DR

This paper uses data mining on over 168,000 crystallographic reports to optimize element oxidation states, revealing discrepancies with traditional chemistry and aiding inorganic materials discovery.

Contribution

It introduces a data-driven method to assign oxidation states in inorganic materials, improving upon traditional textbook assignments.

Findings

Identified discrepancies between reported and textbook oxidation states.

Proposed optimized oxidation states to enhance materials discovery.

Demonstrated the utility of data-mined oxidation states in heuristic design.

Abstract

Oxidation states are well-established in chemical science teaching and research. We data-mine more than 168,000 crystallographic reports to find an optimal allocation of oxidation states to each element. In doing so we uncover discrepancies between text-book chemistry and reported charge states observed in materials. We go on to show how the oxidation states we recommend can significantly facilitate materials discovery and heuristic design of novel inorganic compounds.