In the pursuit of materials with exceptional mechanical properties, a machine-learning model is developed to direct the synthetic efforts toward compounds with high hardness by predicting the elastic moduli as a proxy. This approach screens 118 287 compounds compiled in crystal structure databases for the materials with the highest bulk and shear moduli determined by support vector machine regression. Following these models, a ternary rhenium tungsten carbide and a quaternary molybdenum tungsten borocarbide are selected and synthesized at ambient pressure. High-pressure diamond anvil cell measurements corroborate the machine-learning prediction of the bulk modulus with less than 10% error, as well as confirm the ultraincompressible nature of both compounds. Subsequent Vickers microhardness measurements reveal that each compound also has an extremely high hardness exceeding the superhard threshold of 40 GPa at low loads (0.49 N). These results show the effectiveness of materials development through state-of-the-art machine-learning techniques by identifying functional inorganic materials.

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Polycrystalline (Mg(0.9),Fe(0.1))SiO3 post-perovskite was plastically deformed in the diamond anvil cell between 145 and 157 gigapascals. The lattice-preferred orientations obtained in the sample suggest that slip on planes near (100) and (110) dominate plastic deformation under these conditions. Assuming similar behavior at lower mantle conditions, we simulated plastic strains and the contribution of post-perovskite to anisotropy in the D'' region at the Earth core-mantle boundary using numerical convection and viscoplastic polycrystal plasticity models. We find a significant depth dependence of the anisotropy that only develops near and beyond the turning point of a downwelling slab. Our calculated anisotropies are strongly dependent on the choice of elastic moduli and remain hard to reconcile with seismic observations.

Polycrystalline MgGeO3 post-perovskite was plastically deformed in the diamond anvil cell between 104 and 130 gigapascals confining pressure and ambient temperature. In contrast with phenomenological considerations suggesting (010) as a slip plane, lattice planes near (100) became aligned perpendicular to the compression direction, suggesting that slip on (100) or (110) dominated plastic deformation. With the assumption that silicate post-perovskite behaves similarly at lower mantle conditions, a numerical model of seismic anisotropy in the D'' region implies a maximum contribution of post-perovskite to shear wave splitting of 3.7% with an oblique polarization.

Understanding deformation of mineral phases in the lowermost mantle is important for interpreting seismic anisotropy in Earth's interior. Recently, there has been considerable controversy regarding deformation-induced slip in MgSiO(3) post-perovskite. Here, we observe that (001) lattice planes are oriented at high angles to the compression direction immediately after transformation and before deformation. Upon compression from 148 gigapascals (GPa) to 185 GPa, this preferred orientation more than doubles in strength, implying slip on (001) lattice planes. This contrasts with a previous experiment that recorded preferred orientation likely generated during the phase transformation rather than deformation. If we use our results to model deformation and anisotropy development in the D'' region of the lower mantle, shear-wave splitting (characterized by fast horizontally polarized shear waves) is consistent with seismic observations.

Synchrotron X-ray diffraction images are increasingly used to characterize crystallographic preferred orientation distributions (texture) of fine-grained polyphase materials. Diffraction images can be analyzed quantitatively with the Rietveld method as implemented in the software package Materials Analysis Using Diffraction. Here we describe the analysis procedure for diffraction images collected with high energy X-rays for a complex, multiphase shale, and for those collected in situ in diamond anvil cells at high pressure and anisotropic stress.

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