Search for: All records

Through laser-heated diamond anvil cell experiments, we synthesize a series of rubidium superhydrides and explore their properties with synchrotron x-ray powder diffraction and Raman spectroscopy measurements, combined with density functional theory calculations. Upon heating rubidium monohydride embedded in ${\mathrm{H}}_2$at a pressure of 18 GPa, we form ${\mathrm{RbH}}_9\text{−}\mathrm{I}$, which is stable upon decompression down to 8.7 GPa, the lowest stability pressure of any known superhydride. At 22 GPa, another polymorph, ${\mathrm{RbH}}_9\text{−}\mathrm{II}$is synthesised at high temperature. Unique to the Rb-H system among binary metal hydrides is that further compression does not promote the formation of polyhydrides with higher hydrogen content. Instead, heating above 87 GPa yields ${\mathrm{RbH}}_5$, which exhibits two polymorphs ( ${\mathrm{RbH}}_5\text{−}\mathrm{I}$and ${\mathrm{RbH}}_5\text{−}\mathrm{II}$). All of the crystal structures comprise a complex network of quasimolecular ${\mathrm{H}}_2$units and ${\mathrm{H}}^{-}$anions, with ${\mathrm{RbH}}_5$providing the first experimental evidence of linear ${\mathrm{H}}_3^{-}$anions. Published by the American Physical Society2025 

First PXRD assisted crystal structure prediction method that can correct for temperature, strain, and choice of computational method. 

Abstract The X₂MH₆family, consisting of an electropositive cation Xⁿ⁺and a main group metal M octahedrally coordinated by hydrogen, have been identified as promising templates for high‐temperature conventional superconductivity. Herein, we analyze the electronic structure of two members of this family, Mg₂IrH₆and Ca₂IrH₆, showing why the former may possess superconducting properties rivaling those of the cuprates, whereas the latter does not. Within Mg₂IrH₆the vibrations of the anions IrH₆⁴⁻anions are key for the superconducting mechanism, and they induce coupling in the set of orbitals, which are antibonding between the H 1sand the Ir or orbitals. Because calcium possesses low‐lyingd‐orbitals, →Cadback‐donation is preferred, quenching the superconductivity. Our analysis explains why high critical temperatures were only predicted for second or third row X metal atoms, and may provide rules for identifying likely high‐temperature superconductors in other systems where the antibonding anionic states are filled. 

Abstract Machine learning models are susceptible to being misled by biases in training data that emphasize incidental correlations over the intended learning task. In this study, we demonstrate the impact of data bias on the performance of a machine learning model designed to predict the likelihood of synthesizability of crystal compounds. The model performs a binary classification on labeled crystal samples. Despite using the same architecture for the machine learning model, we showcase how the model’s learning and prediction behavior differs once trained on distinct data. We use two data sets for illustration: a mixed-source data set that integrates experimental and computational crystal samples and a single-source data set consisting of data exclusively from one computational database. We present simple procedures to detect data bias and to evaluate its effect on the model’s performance and generalization. This study reveals how inconsistent, unbalanced data can propagate bias, undermining real-world applicability even for advanced machine learning techniques.