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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 

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. 

In light of breakthroughs in superconductivity under high pressure, and considering that record critical temperatures (T_cs) across various systems have been achieved under high pressure, the primary challenge for higher T_cshould no longer solely be to increase T_cunder extreme conditions but also to reduce, or ideally eliminate, the need for applied pressure in retaining pressure-induced or -enhanced superconductivity. The topological semiconductor Bi_0.5Sb_1.5Te₃(BST) was chosen to demonstrate our approach to addressing this challenge and exploring its intriguing physics. Under pressures up to ~50 GPa, three superconducting phases (BST-I, -II, and -III) were observed. A superconducting phase in BST-I appears at ~4 GPa, without a structural transition, suggesting the possible topological nature of this phase. Using the pressure-quench protocol (PQP) recently developed by us, we successfully retained this pressure-induced phase at ambient pressure and revealed the bulk nature of the state. Significantly, this demonstrates recovery of a pressure-quenched sample from a diamond anvil cell at room temperature with the pressure-induced phase retained at ambient pressure. Other superconducting phases were retained in BST-II and -III at ambient pressure and subjected to thermal and temporal stability testing. Superconductivity was also found in BST with T_cup to 10.2 K, the record for this compound series. While PQP maintains superconducting phases in BST at ambient pressure, both depressurization and PQP enhance its T_c, possibly due to microstructures formed during these processes, offering an added avenue to raise T_c. These findings are supported by our density-functional theory calculations. 

Metastable materials are abundant in nature and technology, showcasing remarkable properties that inspire innovative materials design. However, traditional crystal structure prediction methods, which rely solely on energetic factors to determine a structure’s fitness, are not suitable for predicting the vast number of potentially synthesizable phases that represent a local minimum corresponding to a state in thermodynamic equilibrium. Here, we present a new approach for the prediction of metastable phases with specific structural features and interface this method with the XTALOPT evolutionary algorithm. Our method relies on structural features that include the local crystalline order (e.g, the coordination number or chemical environment), and symmetry (e.g, Bravais lattice and space group) to filter the breeding pool of an evolutionary crystal structure search. The effectiveness of this approach is benchmarked on three known metastable systems: XeN8, with a two-dimensional polymeric nitrogen sublattice, brookite TiO2, and a high pressure BaH4 phase, which was recently characterized. Additionally, a newly predicted metastable melaminate salt, P1̅ WC3N6, was found to possess an energy that is lower than that of two phases proposed in a recent computational study. The method presented here could help in identifying the structures of compounds that have already been synthesized, and in developing new synthesis targets with desired properties.