Structure, Stability and Superconductivity of N-doped Lutetium Hydrides at kbar Pressures

The structure of the material responsible for the room temperature and near ambient pressure superconductivity reported in an N-doped lutetium hydride [Nature, 615, 244 (2023)] has not been conclusively determined. Herein, density functional theory calculations are performed in an attempt to uncover what it might be. Guided by a range of strategies including crystal structure prediction and modifications of existing structure types, we present an array of Lu-N-H phases that are dynamically stable at experimentally relevant pressures. Although none of the structures found are thermodynamically stable, and none are expected to remain superconducting above 17 K at 10 kbar, a number of metallic compounds with fcc Lu lattices -- as suggested by the experimental X-ray diffraction measurements of the majority phase -- are identified. The system whose calculated equation of states matches best with that measured for the majority phase is fluorite-type LuH2, whose 10 kbar superconducting critical temperature was estimated to be 0.09 K using the Allen-Dynes modified McMillan equation.Comment: 11 pages, 8 figure

Barium in High Oxidation States in Pressure-Stabilized Barium Fluorides

The oxidation state of an element influences its chemical behavior of reactivity and bonding. Finding unusual oxidation state of elements is a theme of eternal pursuit. As labeled by an alkali-earth metal, barium (Ba) invariably exhibits an oxidation state of +2 by a loss of two 6s valence electrons while its inner 5p closed shell is known to remain intact. Here, we show through the reaction with fluorine (F) at high pressure that Ba exhibits a hitherto unexpected high oxidation state greater than +2 in three pressure-stabilized F-rich compounds BaF₃, BaF₄, and BaF₅, where Ba takes on the role of a 5p element by opening up its inert 5p shell. Interestingly enough, these pressure-stabilized Ba fluorides share common structural features of Ba-centered polyhedrons but exhibit a diverse variety of electronic properties showing semiconducting, metallic, and even magnetic behaviors. Our work modifies the traditional knowledge on the chemistry of alkali-earth Ba element established at ambient pressure and highlights the major role of pressure played in tuning the oxidation state of elements

A hypervalent and cubically coordinated molecular phase of IF8 predicted at high pressure

Up to now, the maximum coordination number of iodine is seven in neutral iodine heptafluoride (IF 7 ) and eight in anionic octafluoride (IF 8− ). Here, we explore pressure as a method for realizing new hypercoordinated iodine compounds. First-principles swarm structure calculations have been used to predict the high-pressure and T → 0 K phase diagram of binary iodine fluorides. The investigated compounds are predicted to undergo complex structural phase transitions under high pressure, accompanied by various semiconductor to metal transitions. The pressure induced formation of a neutral octafluoride compound, IF 8 , consisting of eight-coordinated iodine is one of several unprecedented predicted structures. In sharp contrast to the square antiprismatic structure in IF 8− , IF 8 , which is dynamically unstable under atmospheric conditions, is stable and adopts a quasi-cube molecular configuration with R3& symmetry at 300 GPa. The metallicity of IF 8 originates from a hole in the fluorine 2p-bands that dominate the Fermi surface. The highly unusual coordination sphere in IF 8 at 300 GPa is a consequence of the 5d levels of iodine coming down and becoming part of the valence, where they mix with iodine\u27s 5s and 5p levels and engage in chemical bonding. The valence expansion of iodine under pressure effectively makes IF 8 not only hypercoordinated, but also hypervalent

Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning

We propose a novel self-supervised method, referred to as Video Cloze Procedure (VCP), to learn rich spatial-temporal representations. VCP first generates “blanks” by withholding video clips and then creates “options” by applying spatio-temporal operations on the withheld clips. Finally, it fills the blanks with “options” and learns representations by predicting the categories of operations applied on the clips. VCP can act as either a proxy task or a target task in self-supervised learning. As a proxy task, it converts rich self-supervised representations into video clip operations (options), which enhances the flexibility and reduces the complexity of representation learning. As a target task, it can assess learned representation models in a uniform and interpretable manner. With VCP, we train spatial-temporal representation models (3D-CNNs) and apply such models on action recognition and video retrieval tasks. Experiments on commonly used benchmarks show that the trained models outperform the state-of-the-art self-supervised models with significant margins