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 experi...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 eightmetals asWsubstituents 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.Amongthe substituents considered,Ta provides thewidest range of predicted stable concentrations and leads to the most considerable changes inmechanical properties.The vacuumless arc plasmamethod 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.展开更多
基金platform(Sber,Moscow,Russia)used for calculations with GNN models.DFT calculations were carried out using Skoltech supercomputer Zhores.Experiments on the vacuumless synthesis of higher tungsten boride were carried out with support from the Ministry of Science,Higher Education of the Russian Federation in part of the Science program(Project FSWW-2025-0003).
文摘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 eightmetals asWsubstituents 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.Amongthe substituents considered,Ta provides thewidest range of predicted stable concentrations and leads to the most considerable changes inmechanical properties.The vacuumless arc plasmamethod 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.
