Free surfaces recast superconductivity in few-monolayer MgB2: Combined first-principles and ARPES demonstration

Surface inducing high-temperature superconductivity in layered metal carborides Li2BC3 and LiBC by metallizing σ electrons

Metallizing σ electrons provides a promising route to design high-temperature superconducting materials, such as MgB2 and high-pressure hydrides. Here, we focus on two MgB2-like layered carborides Li2BC3 and LiBC; their bulk does not have superconductivity because the B-C σ states are far away from the Fermi level (EF), however, based on first-principles calculations, we found that when their bulk systems are cleaved into surfaces with B-C termination, high Tc of ∼80 K could be observed in the exposed B-C layer on the surfaces. Detailed analysis reveals that surface symmetry reduction, due to lattice periodic breaking, not only introduces hole self-doping into surface B-C layers and shifts the σ-bonding states towards the EF - associated with emergent large electronic occupation, but also makes in-plane stretching modes on the surface layer experience significant softness. The enhanced σ states and softened phonon modes work to produce strong coupling, thus yielding high-Tc surface superconductivity, which distinctly differs from the superconducting features of the MgB2 film, which generates phonon stiffness accompanied by suppressed superconductivity. Our findings undoubtedly provide a novel platform to realize high-Tc surface superconductivity, and also clearly elucidate the microscopic mechanism of surface-enhanced superconductivity in favor of creating more high-Tc surface superconductors among MgB2-like layered materials.

Prediction of novel two-dimensional Dirac nodal line semimetals in Al2B2 and AlB4 monolayers

Topological semimetal phases in two-dimensional (2D) materials have gained widespread interest due to their potential applications in novel nanoscale devices. Despite the growing number of studies on 2D topological nodal lines (NLs), candidates with significant topological features that combine nontrivial topological semimetal phase with superconductivity are still rare. Herein, we predict Al₂B₂ and AlB₄ monolayers as new 2D nonmagnetic Dirac nodal line semimetals with several novel features. Our extensive electronic structure calculations combined with analytical studies reveal that, in addition to multiple Dirac points, these 2D configurations host various highly dispersed NLs around the Fermi level, all of which are semimetal states protected by time-reversal and in-plane mirror symmetries. The most intriguing NL in Al₂B₂ encloses the K point and crosses the Fermi level, showing a considerable dispersion and thus providing a fresh playground to explore exotic properties in dispersive Dirac nodal lines. More strikingly, for the AlB₄ monolayer, we provide the first evidence for a set of 2D nonmagnetic open type-II NLs coexisting with superconductivity at a rather high transition temperature. The coexistence of superconductivity and nontrivial band topology in AlB₄ not only makes it a promising material to exhibit novel topological superconducting phases, but also a rather large energy dispersion of type-II nodal lines in this configuration may offer a platform for the realization of novel topological features in the 2D limit.

High-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials

Beyond the conventional trial-and-error method, machine learning offers a great opportunity to accelerate the discovery of functional materials, but still often suffers from difficulties such as limited materials data and the unbalanced distribution of target properties. Here, we propose the ab initio Bayesian active learning method that combines active learning and high-throughput ab initio calculations to accelerate the prediction of desired functional materials with ultrahigh efficiency and accuracy. We apply it as an instance to a large family (3119) of two-dimensional hexagonal binary compounds with unbalanced materials properties, and accurately screen out the materials with maximal electric polarization and proper photovoltaic band gaps, respectively, whereas the computational costs are significantly reduced by only calculating a few tenths of the possible candidates in comparison with a random search. This approach shows the enormous advantages for the cases with unbalanced distribution of target properties. It can be readily applied to seek a broad range of advanced materials.

Semiconducting MnB5monolayer as a potential photovoltaic material

Exploring new two-dimensional (2D) materials is of great significance for both basic research and practical applications. Although boron can form various 3D and 2D allotropes due to its ease of forming multi-center bonds, the coexistence of honeycomb and kagome boron structures has never been observed in any 2D material yet. In this article we apply first-principle swarm structural searches to predict the existence of a stable MnB₅structure, consisting of a sandwich of honeycomb and kagome borophenes. More interestingly, a MnB₅nanosheet is a semiconductor with a band gap of 1.07 eV and a high optical absorption in a broad band, which satisfies the requirements of a very good photovoltaic material. Upon moderate strain, MnB₅undergoes a conversion from an indirect to a direct band gap semiconductor. The power conversion efficiency of a heterostructure solar cell made of MnB₅is up to 18%. The MnB₅nanosheet shows a robust dynamical and thermal stability, stemming from the presence of intra- and interlayer multi-center σ and π bonds. These characteristics make MnB₅a promising photovoltaic material.

Voting Data-Driven Regression Learning for Accelerating Discovery of Advanced Functional Materials and Applications to Two-Dimensional Ferroelectric Materials

Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to poor performance. Here, we develop a new voting data-driven method that could generally improve the performance of the regression learning model for accurately predicting properties of materials. We apply it to investigate a large family (2135) of two-dimensional hexagonal binary compounds focusing on ferroelectric properties and find that the performance of the model for electric polarization is indeed greatly improved, where 38 stable ferroelectrics with out-of-plane polarization including 31 metals and 7 semiconductors are screened out. By unsupervised learning, actionable information such as how the number and orbital radius of valence electrons, ionic polarizability, and electronegativity of constituent atoms affect polarization was extracted. Our voting data-driven method not only reduces the size of materials data for constructing a reliable learning model but also enables one to make precise predictions for targeted functional materials.

MoB2: a new multifunctional transition metal diboride monolayer

Several layered transition metal borides can now be realized by a simple and general fabrication method (Fokwa et al 2018 Adv. Mater. 30 1704181), inspiring our interest to transition metal borides monolayer. Herein, we predict a new two-dimensional (2D) transition metal diboride MoB₂ monolayer (ML) and study its intrinsic mechanical, thermal, electronic, and transport properties. The MoB₂ ML has isotropic mechanic properties along the zigzag and armchair directions with a large Young's stiffness, and has an ultralow room-temperature thermal conductivity. The Mo atoms dominate the metallic nature of MoB₂ ML. It shows an obvious electrical anisotropy and a current-limiting behavior. Our findings suggest that MoB₂ ML is a promising multifunctional material used in ultrathin high-strength mechanical materials, heat insulating materials, electrical-anisotropy-based materials, and current limiters. It is helpful for the experimentalists to further prepare and utilize the transition metal diboride 2D materials.

Hydrogen-Induced High-Temperature Superconductivity in Two-Dimensional Materials: The Example of Hydrogenated Monolayer MgB_{2}

Hydrogen-based compounds under ultrahigh pressure, such as the polyhydrides H_{3}S and LaH_{10}, superconduct through the conventional electron-phonon coupling mechanism to attain the record critical temperatures known to date. Here we exploit the intrinsic advantages of hydrogen to strongly enhance phonon-mediated superconductivity in a completely different system, namely, a two-dimensional material with hydrogen adatoms. We find that van Hove singularities in the electronic structure, originating from atomiclike hydrogen states, lead to a strong increase of the electronic density of states at the Fermi level, and thus of the electron-phonon coupling. Additionally, the emergence of high-frequency hydrogen-related phonon modes in this system boosts the electron-phonon coupling further. As a concrete example, we demonstrate the effect of hydrogen adatoms on the superconducting properties of monolayer MgB_{2}, by solving the fully anisotropic Eliashberg equations, in conjunction with a first-principles description of the electronic and vibrational states, and their coupling. We show that hydrogenation leads to a high critical temperature of 67 K, which can be boosted to over 100 K by biaxial tensile strain.

Negative differential conductance effect and electrical anisotropy of 2D ZrB2 monolayers

Two-dimensional (2D) metal-diboride ZrB₂ monolayers was predicted theoretically as a stable new electronic material (Lopez-Bezanilla 2018 Phys. Rev. Mater. 2 011002). Here, we investigate its electronic transport properties along the zigzag (z-ZrB₂) and armchair (a-ZrB₂) directions, using the density functional theory and non-equilibrium Green's function methods. Under low biases, the 2D ZrB₂ shows a similar electrical transport along zigzag and armchair directions as electric current propagates mostly via the metallic Zr-Zr bonds. However, it shows an electrical anistropy under high biases, and its I-V curves along zigzag and armchair directions diverge as the bias voltage is higher than 1.4 V, as more directional B-B transmission channels are opened. Importantly, both z-ZrB₂ and a-ZrB₂ show a pronounced negative differential conductance (NDC) effect and hence they can be promising for the use in NDC-based nanodevices.

Superconductivity in Li-intercalated bilayer arsenene and hole-doped monolayer arsenene: a first-principles prediction

Using first-principles calculations, we find Li-intercalated bilayer arsenene with AB stacking is dynamically stable, which is different from pristine bilayer with AA stacking. Electron-phonon coupling of the stable Li-intercalated bilayer arsenene are dominated by the low frequency vibrational modes (E″(1), [Formula: see text](1), E'(1) and acoustic modes) and lead to an superconductivity with T _c  =  8.68 K with isotropical Eliashberg function. Small biaxial tensile strain (2%) can improve T _c to 11.22 K due to the increase of DOS and phonon softening. By considering the fully anisotropic Migdal-Eliashberg theory, T _c are found to be enhanced by 50% and exhibits a single anisotropic gap nature. In addition, considering its nearly flat top valence band which is favorable for high temperature superconductivity, we also explore the superconducting properties of hole-doped monolayer arsenene under different strains. the unstrained monolayer arsenene superconducts at T _c  =  0.22 K with 0.1 hole/cell doping. By applying 3% biaxial strain, T _c can be lifted up strikingly to 6.69 K due to a strong Fermi nesting of the nearly flat band. Then T _c decreases slowly with strain. Our findings provide another insight to realize 2D superconductivity and suggest that the strain is crucial to further enhance the transition temperature.