Spin splitting of dopant edge state in magnetic zigzag graphene nanoribbons.

Spin-ordered electronic states in hydrogen-terminated zigzag nanographene give rise to magnetic quantum phenomena1,2 that have sparked renewed interest in carbon-based spintronics3,4. Zigzag graphene nanoribbons (ZGNRs)-quasi one-dimensional semiconducting strips of graphene bounded by parallel zigzag edges-host intrinsic electronic edge states that are ferromagnetically ordered along the edges of the ribbon and antiferromagnetically coupled across its width1,2,5. Despite recent advances in the bottom-up synthesis of GNRs featuring symmetry protected topological phases6-8 and even metallic zero mode bands9, the unique magnetic edge structure of ZGNRs has long been obscured from direct observation by a strong hybridization of the zigzag edge states with the surface states of the underlying support10-15. Here, we present a general technique to thermodynamically stabilize and electronically decouple the highly reactive spin-polarized edge states by introducing a superlattice of substitutional N-atom dopants along the edges of a ZGNR. First-principles GW calculations and scanning tunnelling spectroscopy reveal a giant spin splitting of low-lying nitrogen lone-pair flat bands by an exchange field (~850 tesla) induced by the ferromagnetically ordered edge states of ZGNRs. Our findings directly corroborate the nature of the predicted emergent magnetic order in ZGNRs and provide a robust platform for their exploration and functional integration into nanoscale sensing and logic devices15-21.

Data-Driven Enhancement of ZT in SnSe-Based Thermoelectric Systems.

Doping and alloying are fundamental strategies to improve the thermoelectric performance of bare materials. However, identifying outstanding elements and compositions for the development of high-performance thermoelectric materials is challenging. In this study, we present a data-driven approach to improve the thermoelectric performance of SnSe compounds with various doping. Based on the newly generated experimental and computational dataset, we built highly accurate predictive models of thermoelectric properties of doped SnSe compounds. A well-designed feature vector consisting of the chemical properties of a single atom and the electronic structures of a solid plays a key role in achieving accurate predictions for unknown doping elements. Using the machine learning predictive models and calculated map of the solubility limit for each dopant, we rapidly screened high-dimensional material spaces of doped SnSe and evaluated their thermoelectric properties. This data-driven search provided overall strategies to optimize and improve the thermoelectric properties of doped SnSe compounds. In particular, we identified five dopant candidate elements (Ge, Pb, Y, Cd, and As) that provided a high ZT exceeding 2.0 and proposed a design principle for improving the ZT by Sn vacancies depending on the doping elements. Based on the search, we proposed yttrium as a new high-ZT dopant for SnSe with experimental confirmations. Our research is expected to lead to novel high-ZT thermoelectric material candidates and provide cutting-edge research strategies for materials design and extraction of design principles through data-driven research.

Polycrystalline SnSe with a thermoelectric figure of merit greater than the single crystal.

Thermoelectric materials generate electric energy from waste heat, with conversion efficiency governed by the dimensionless figure of merit, ZT. Single-crystal tin selenide (SnSe) was discovered to exhibit a high ZT of roughly 2.2-2.6 at 913 K, but more practical and deployable polycrystal versions of the same compound suffer from much poorer overall ZT, thereby thwarting prospects for cost-effective lead-free thermoelectrics. The poor polycrystal bulk performance is attributed to traces of tin oxides covering the surface of SnSe powders, which increases thermal conductivity, reduces electrical conductivity and thereby reduces ZT. Here, we report that hole-doped SnSe polycrystalline samples with reagents carefully purified and tin oxides removed exhibit an ZT of roughly 3.1 at 783 K. Its lattice thermal conductivity is ultralow at roughly 0.07 W m-1 K-1 at 783 K, lower than the single crystals. The path to ultrahigh thermoelectric performance in polycrystalline samples is the proper removal of the deleterious thermally conductive oxides from the surface of SnSe grains. These results could open an era of high-performance practical thermoelectrics from this high-performance material.

Exceptionally High Average Power Factor and Thermoelectric Figure of Merit in n-type PbSe by the Dual Incorporation of Cu and Te.

Thermoelectric materials with high average power factor and thermoelectric figure of merit (ZT) has been a sought-after goal. Here, we report new n-type thermoelectric system CuxPbSe0.99Te0.01 (x = 0.0025, 0.004, and 0.005) exhibiting record-high average ZT ∼ 1.3 over 400-773 K ever reported for n-type polycrystalline materials including the state-of-the-art PbTe. We concurrently alloy Te to the PbSe lattice and introduce excess Cu to its interstitial voids. Their resulting strong attraction facilitates charge transfer from Cu atoms to the crystal matrix significantly. It follows the increased carrier concentration without damaging its mobility and the consequently improved electrical conductivity. This interaction also increases effective mass of electron in the conduction band according to DFT calculations, thereby raising the magnitude of Seebeck coefficient without diminishing electrical conductivity. Resultantly, Cu0.005PbSe0.99Te0.01 attains an exceptionally high average power factor of ∼27 µW cm-1 K-2 from 400 to 773 K with a maximum of ∼30 µW cm-1 K-2 at 300 K, the highest among all n- and p-type PbSe-based materials. Its ∼23 µW cm-1 K-2 at 773 K is even higher than ∼21 µW cm-1 K-2 of the state-of-the-art n-type PbTe. Interstitial Cu atoms induce the formation of coherent nanostructures. They are highly mobile, displacing Pb atoms from the ideal octahedral center and severely distorting the local microstructure. This significantly depresses lattice thermal conductivity to ∼0.2 Wm-1 K-1 at 773 K below the theoretical lower bound. The multiple effects of the dual incorporation of Cu and Te synergistically boosts a ZT of Cu0.005PbSe0.99Te0.01 to ∼1.7 at 773 K.

Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction.

The open-access material databases allowed us to approach scientific questions from a completely new perspective with machine learning methods. Here, on the basis of open-access databases, we focus on the classical band gap problem for predicting accurately the band gap of a crystalline compound using a machine learning approach with newly developed tuplewise graph neural networks (TGNN), which is devised to automatically generate input representation of crystal structures in tuple types and to exploit crystal-level properties as one of the input features. Our method brings about a highly accurate prediction of the band gaps at hybrid functionals and GW approximation levels for multiple material data sets without heavy computational cost. Furthermore, to demonstrate the applicability of our prediction model, we provide a data set of GW band gaps for 45835 materials predicted by TGNN posing higher accuracy than standard density functional theory calculations.

Topological Phases in Cove-Edged and Chevron Graphene Nanoribbons: Geometric Structures, [Formula: see text]2 Invariants, and Junction States.

Graphene nanoribbons (GNRs) have recently been shown by Cao, Zhao, and Louie [Cao, T.; Zhao, F.; Louie, S. G. Phys. Rev. Lett. 2017, 119, 076401] to possess distinct topological phases in general, characterized by a [Formula: see text]2 invariant. Cove-edged and chevron GNRs moreover are chemically and structurally diverse, quasi-one-dimensional (1D) nanostructures whose structure and electronic properties can be rationally controlled by bottom-up synthesis from precursor molecules. We derive the value of the topological invariant of the different types of cove-edged and chevron GNRs, and we investigate the electronic properties of various junctions formed by these GNRs, as well as such GNRs with the more common armchair or zigzag GNRs. We study the topological junction states at the interface of two topologically distinct segments. For an isolated GNR having two ends of different terminations, topological end states are shown to develop only at the topologically nontrivial end. Our work extends the explicit categorization of topological invariants of GNRs beyond the previously demonstrated armchair GNRs and provides new design rules for novel GNR junctions as well as future GNR-based nanoelectronic devices.

Orbitally Matched Edge-Doping in Graphene Nanoribbons.

A series of trigonal planar N-, O-, and S-dopant atoms incorporated along the convex protrusion lining the edges of bottom-up synthesized chevron graphene nanoribbons (cGNRs) induce a characteristic shift in the energy of conduction and valence band edge states along with a significant reduction of the band gap of up to 0.3 eV per dopant atom per monomer. A combination of scanning probe spectroscopy and density functional theory calculations reveals that the direction and the magnitude of charge transfer between the dopant atoms and the cGNR backbone are dominated by inductive effects and follow the expected trend in electronegativity. The introduction of heteroatom dopants with trigonal planar geometry ensures an efficient overlap of a p-orbital lone-pair centered on the dopant atom with the extended π-system of the cGNR backbone effectively extending the conjugation length. Our work demonstrates a widely tunable method for band gap engineering of graphene nanostructures for advanced electronic applications.

Manifestation of axion electrodynamics through magnetic ordering on edges of a topological insulator.

Because topological surface states of a single-crystal topological insulator can exist on all surfaces with different crystal orientations enclosing the crystal, mutual interactions among those states contiguous to each other through edges can lead to unique phenomena inconceivable in normal insulators. Here we show, based on a first-principles approach, that the difference in the work function between adjacent surfaces with different crystal-face orientations generates a built-in electric field around facet edges of a prototypical topological insulator such as Bi2Se3. Owing to the topological magnetoelectric coupling for a given broken time-reversal symmetry in the crystal, the electric field, in turn, forces effective magnetic dipoles to accumulate along the edges, realizing the facet-edge magnetic ordering. We demonstrate that the predicted magnetic ordering is in fact a manifestation of the axion electrodynamics in real solids.

Machine Learning Prediction Models for Solid Electrolytes Based on Lattice Dynamics Properties.

Recently, machine-learning approaches have accelerated computational materials design and the search for advanced solid electrolytes. However, the predictors are currently limited to static structural parameters, which may not fully account for the dynamic nature of ionic transport. In this study, we meticulously curated features considering dynamic properties and developed machine-learning models to predict the ionic conductivity, σ, of solid electrolytes. We compiled 14 phonon-related descriptors from first-principles phonon calculations along with 16 descriptors related to the structure and electronic properties. Our logistic regression classifiers exhibit an accuracy of 93%, while the random forest regression model yields a root-mean-square error for log(σ) of 1.179 S/cm and R2 of 0.710. Notably, phonon-related features are essential for estimating the ionic conductivities in both models. Furthermore, we applied our prediction model to screen 264 Li-containing materials and identified 11 promising candidates as potential superionic conductors.

Tunneling-induced spectral broadening of a single atom in a three-dimensional optical lattice.

We have investigated the spectral broadening in the near-resonance fluorescence spectrum of a single rubidium atom trapped in a three-dimensional (3D) optical lattice in a strong Lamb-Dicke regime. Besides the strong Rayleigh peak, the spectrum exhibited weak Stokes and anti-Stokes Raman sidebands. The line width of the Rayleigh peak for low potential depths was well explained by matter-wave tunneling between the first-two lowest vibrational states of 3D anisotropic harmonic potentials of adjacent local minima of the optical lattice.

Direct strain correlations at the single-atom level in three-dimensional core-shell interface structures.

Nanomaterials with core-shell architectures are prominent examples of strain-engineered materials. The lattice mismatch between the core and shell materials can cause strong interface strain, which affects the surface structures. Therefore, surface functional properties such as catalytic activities can be designed by fine-tuning the misfit strain at the interface. To precisely control the core-shell effect, it is essential to understand how the surface and interface strains are related at the atomic scale. Here, we elucidate the surface-interface strain relations by determining the full 3D atomic structure of Pd@Pt core-shell nanoparticles at the single-atom level via atomic electron tomography. Full 3D displacement fields and strain profiles of core-shell nanoparticles were obtained, which revealed a direct correlation between the surface and interface strain. The strain distributions show a strong shape-dependent anisotropy, whose nature was further corroborated by molecular statics simulations. From the observed surface strains, the surface oxygen reduction reaction activities were predicted. These findings give a deep understanding of structure-property relationships in strain-engineerable core-shell systems, which can lead to direct control over the resulting catalytic properties.

Hierarchical On-Surface Synthesis of Graphene Nanoribbon Heterojunctions.

Bottom-up graphene nanoribbon (GNR) heterojunctions are nanoscale strips of graphene whose electronic structure abruptly changes across a covalently bonded interface. Their rational design offers opportunities for profound technological advancements enabled by their extraordinary structural and electronic properties. Thus far, the most critical aspect of their synthesis, the control over sequence and position of heterojunctions along the length of a ribbon, has been plagued by randomness in monomer sequences emerging from step-growth copolymerization of distinct monomers. All bottom-up GNR heterojunction structures created so far have exhibited random sequences of heterojunctions and, while useful for fundamental scientific studies, are difficult to incorporate into functional nanodevices as a result. In contrast, we describe a hierarchical fabrication strategy that allows the growth of bottom-up GNRs that preferentially exhibit a single heterojunction interface rather than a random statistical sequence of junctions along the ribbon. Such heterojunctions provide a viable platform that could be directly used in functional GNR-based device applications at the molecular scale. Our hierarchical GNR fabrication strategy is based on differences in the dissociation energies of C-Br and C-I bonds that allow control over the growth sequence of the block copolymers from which GNRs are formed and consequently yields a significantly higher proportion of single-junction GNR heterostructures. Scanning tunneling spectroscopy and density functional theory calculations confirm that hierarchically grown heterojunctions between chevron GNR (cGNR) and binaphthyl-cGNR segments exhibit straddling Type I band alignment in structures that are only one atomic layer thick and 3 nm in width.

Controlling half-metallicity of graphene nanoribbons by using a ferroelectric polymer.

On the basis of first-principles computational approaches, we present a new method to drive zigzag graphene nanoribons (ZGNRs) into the half-metallic state using a ferroelectric material, poly(vinylidene fluoride) (PVDF). Owing to strong dipole moments of PVDFs, the ground state of the ZGNR becomes half-metallic when a critical coverage of PVDFs is achieved on the ZGNR. Since ferroelectric polymers are physisorbed, the direction of the dipole field in PVDFs can be rotated by relatively small external electric fields, and the switching between half-metallic and insulating states may be achieved. Our results suggest that, without excessively large external gate electric fields, half-metallic states of ZGNRs are realizable through the deposition of ferroelectric polymers and their electronic and magnetic properties are controllable via noninvasive mutual interactions.