Discovery of chemically modified higher tungsten boride by means of hybrid GNN/DFT approach

Abstract

High-throughput search for new crystal structures is extensively assisted by data-driven solutions. Here we address their prospects for more narrowly focused applications in a data-efficient manner. To verify and experimentally validate the proposed approach, we consider the structure of higher tungsten borides, WB_4.2, and eight metals as W substituents to set a search space comprising 375k+ inequivalent crystal structures for solid solutions. Their thermodynamic properties are predicted with errors of a few meV/atom using graph neural networks fine-tuned on the DFT-derived properties of ca. 200 entries. Among the substituents considered, Ta provides the widest range of predicted stable concentrations and leads to the most considerable changes in mechanical properties. The vacuumless arc plasma method is used to perform synthesis of higher tungsten borides with different concentrations of Ta. Vickers hardness of WB_5-x samples with different Ta contents is measured, showing increase in hardness.