Search

Auxetic two-dimensional (2D) materials provide a promising platform for biomedicine, sensors, and many other applications at the nanoscale. In this work, utilizing a hypothesis-based data-driven approache, we identify multiple materials with remarkable in-plane auxetic behavior in a family of buckled monolayer 2D materials. These materials are transition metal selenides and transition metal halides with the stoichiometry MX (M = V, Cr, Mn, Fe, Co, Cu, Zn, Ag, and X = Se, Cl, Br, I). First-principles calculations reveal that the desirable auxetic behavior of these 2D compounds originates from the interplay between the buckled 2D structure and the weak metal–metal interaction determined by their electronic structures. We observe that the Poisson’s ratio is sensitive to magnetic order and the amount of uniaxial stress applied. A transition from positive Poisson’s ratio (PPR) to negative Poisson’s ratio (NPR) for a subgroup of MX compounds under large uniaxial stress is predicted. The work provides a guideline for the future design of 2D auxetic materials at the nanoscale.

We propose a novel class of two-dimensional (2D) Dirac materials in the MX family (M = Be, Mg, Zn and Cd, X = Cl, Br and I), which exhibit graphene-like band structures with linearly-dispersing Dirac-cone states over large energy scales (0.8–1.8 eV) and ultra-high Fermi velocities comparable to graphene. Spin-orbit coupling opens sizable topological band gaps so that these compounds can be effectively classified as quantum spin Hall insulators. The electronic and topological properties are found to be highly tunable and amenable to modulation via anion-layer substitution and vertical electric field. Electronic structures of several members of the family are shown to host a Van-Hove singularity (VHS) close to the energy of the Dirac node. The enhanced density-of-states associated with these VHSs could provide a mechanism for inducing topological superconductivity. The presence of sizable band gaps, ultra-high carrier mobilities, and small effective masses makes the MX family promising for electronics and spintronics applications.

Silver nanostructures with hierarchical porosities of multiple length scales have been synthesized through electrochemical reduction of silver benzenethiolate nanoboxes. The porous Ag nanostructures exhibit superior catalytic performance toward electrochemical reduction of CO2. The Faradaic efficiency of reducing CO2 to CO can be close to 100% at high cathodic potentials, benefiting from the readsorbed benzenethiolate ions on the Ag surface that can suppress the hydrogen evolution reaction (HER). Density functional theory calculations using the SCAN functional reveal that the disfavored H binding on the benzenethiolate-modified Ag surface is responsible for inhibiting the HER. The mass-specific activity of CO2 reduction can be over 500 A/g because the multiple-scale porosities maximize the diffusion of reactive species to and away from the Ag surface. The unique multiscale porosities and surface modification of the as-synthesized Ag nanostructures make them a class of promising catalysts for electrochemical reduction of CO2 in protic electrolytes to achieve maximum activity and selectivity.

Discovery and design of two-dimensional (2D) materials with suitable band gaps and high carrier mobility is of vital importance for photonics, optoelectronics, and high-speed electronics. In this work, based on first principles calculations using density functional theory (DFT) with PBE and HSE functionals, we introduce a family of monolayer isostructural semiconducting tellurides M2N2Te8, with M = {Ti, Zr, Hf} and N= {Si, Ge}. These compounds have been identified to possess direct band gaps from 1.0 eV to 1.31 eV, which are well suited for photonics and optoelectronics applications. Additionally, anisotropic in-plane transport behavior is observed and small electron and hole (0.11 - 0.15 me) effective masses are identified along the dominant transport direction. Ultra-high carrier mobility is predicted for this family of 2D compounds which host great promise for potential applications in high-speed electronic devices. Detailed analysis of electronic structures reveals the origins of the promising properties of this unique class of 2D telluride materials., Comment: 15 pages, 3 figures

Incorporation of physical principles in a network-based machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for materials science and condensed matter physics. In this work, as inspired by the Pauling rule, we propose that structure motifs (polyhedral formed by cations and surrounding anions) in inorganic crystals can serve as a central input to a machine learning framework for crystalline inorganic materials. Taking metal oxides as examples, we demonstrated that, an unsupervised learning algorithm Motif2Vec is able to convert the presence of structure motifs and their connections in a large set of crystalline compounds into unique vector representations. The connections among complex materials can be largely determined by the presence of different structure motifs and their clustering information are identified by our Motif2Vec algorithm. To demonstrate the novel use of structure motif information, we show that a motif-centric learning framework can be effectively created by combining motif information with the recently developed atom-based graph neural networks to form an atom-motif dual graph network (AMDNet). Taking advantage of node and edge information on both atomic and motif level, the AMDNet is more accurate than an atom graph network in predicting electronic structure related material properties of metal oxides such as band gaps. The work illustrates the route toward fundamental design of graph neural network learning architecture for complex material properties by incorporating beyond-atom physical principles., 10 pages, 4 figures, 1 table

Biaxial deformation of suspended membranes widely exists and is used in nanoindentation to probe elastic properties of structurally isotropic two-dimensional (2D) materials. However, the elastic properties and, in particular, the fracture behaviors of anisotropic 2D materials remain largely unclarified in the case of biaxial deformation. MoTe2 is a polymorphic 2D material with both isotropic (2H) and anisotropic (1T′ and Td) phases and, therefore, an ideal system of single-stoichiometric materials with which to study these critical issues. Here, we report the elastic properties and fracture behaviors of biaxially deformed, polymorphic MoTe2 by combining temperature-variant nanoindentation and first-principles calculations. It is found that due to similar atomic bonding, the effective moduli of the three phases deviate by less than 15%. However, the breaking strengths of distorted 1T′ and Td phases are only half the value of 2H phase due to their uneven distribution of bonding strengths. Fractures of both ...

Recent years have witnessed tremendous success in the discovery of topological states of matter. Particularly, sophisticated theoretical methods in time-reversal-invariant topological phases have been developed, leading to the comprehensive search of crystal database and the prediction of thousands of new topological materials. In contrast, the discovery of magnetic topological phases that break time reversal is still limited to several exemplary materials because the coexistence of magnetism and topological electronic band structure is rare in a single compound. To overcome this challenge, we propose an alternative approach to realize the quantum anomalous Hall (QAH) effect, a typical example of magnetic topological phase, via engineering two-dimensional (2D) magnetic van der Waals heterojunctions. Instead of a single magnetic topological material, we search for the combinations of two 2D (typically trivial) magnetic insulator compounds with specific band alignment so that they can together form a type-III heterojunction with topologically non-trivial band structure. By combining the data-driven materials search, first principles calculations, and the symmetry-based analytical models, we identify 8 type-III heterojunctions consisting of 2D ferromagnetic insulator materials from a family of 2D monolayer MXY compounds (M = metal atoms, X = S, Se, Te, Y = F, Cl, Br, I) as a set of candidates for the QAH effect. In particular, we directly calculate the topological invariant (Chern number) and chiral edge states in the MnNF/MnNCl heterojunction with ferromagnetic stacking. This work illustrates how data-driven material science can be combined with symmetry-based physical principles to guide the search for novel heterojunction-based quantum materials hosting the QAH effect and other exotic quantum states in general.

Significance Motivated by the recent achievements of artificial intelligence (AI) in linguistics, we design AI to learn properties of atoms from materials data on its own. Our work realizes knowledge representation of atoms via computers and could serve as a foundational step toward materials discovery and design fully based on machine learning., Exciting advances have been made in artificial intelligence (AI) during recent decades. Among them, applications of machine learning (ML) and deep learning techniques brought human-competitive performances in various tasks of fields, including image recognition, speech recognition, and natural language understanding. Even in Go, the ancient game of profound complexity, the AI player has already beat human world champions convincingly with and without learning from the human. In this work, we show that our unsupervised machines (Atom2Vec) can learn the basic properties of atoms by themselves from the extensive database of known compounds and materials. These learned properties are represented in terms of high-dimensional vectors, and clustering of atoms in vector space classifies them into meaningful groups consistent with human knowledge. We use the atom vectors as basic input units for neural networks and other ML models designed and trained to predict materials properties, which demonstrate significant accuracy.

Alumina (Al2O3) is one of the most widely used ceramic materials for innumerable applications, due to its unique combination of attractive physical and mechanical properties. These intrinsic properties are dictated by the numerous phases that Al2O3 forms and its related phase transformations. Transition metal (TM) cation dopants (iron (Fe), cobalt (Co), nickel (Ni) and manganese (Mn)), even in sparse amounts, have been shown to significantly affect the phase transformation and microstructural evolution of Al2O3. Small concentrations of TM cation dopants have successfully been incorporated to synthesize magnetically active Al2O3, while reducing the θ to α phase transformation temperature by 150 °C, and maintaining the outstanding mechanical properties. In addition, first-principle calculations based on density-functional theory with hybrid functional (HSE06) and the PBE+U methods have provided a mechanistic understanding of the formation energy and magnetism of the TM-doped α and θ phases of Al2O3. The results reveal a potential route for phase transition regulation and external magnetic field-induced texturing of Al2O3 ceramics.

Atomic-level understanding of roles of defect–defect interaction in the bonding of adsorbates on surfaces is critical for tailoring catalysts atom-by-atom and designing new catalysts. Here, from first-principles calculations, we propose a microscopic mechanism for the role of sulfur vacancy–vacancy interaction in hydrogen bonding on surfaces of MoS2, a nonprecious two-dimensional catalyst for hydrogen evolution reaction. We find that before hydrogen adsorption the interaction of a sulfur vacancy with others is repulsive, originating from the antibonding-like coupling of occupied in-gap vacancy states. When the sulfur vacancy is adsorbed by a hydrogen atom, its interaction with other unadsorbed sulfur vacancies becomes attractive, which can be attributed to the decoupling of repulsive vacancy–vacancy interactions and the occupying of bonding-like coupling states between the in-gap vacancy states that are unoccupied before hydrogen adsorption. This repulsive-to-attractive reverse of vacancy–vacancy interact...

The description of the chemical bond between a solid surface and an atom or a molecule is the fundamental basis for understanding a broad range of scientific problems in heterogeneous catalysis, semiconductor device fabrication, and fuel cells. Widespread understandings are based on the molecular orbital theory and focused on the degree of filling of antibonding surface-adsorbate states that weaken bonding on surfaces. The unoccupied antibonding surface-adsorbate states are often tacitly assumed to be irrelevant. Here, we show that the unoccupancy of those antibonding surface-adsorbate states is related to antibonding electron transfer: Electrons that would occupy these antibonding states are transferred to the lower-energy Fermi level. This antibonding electron transfer leads to an energy gain, which plays a critically important role in controlling the trends of bond formation on surfaces that are often inhomogeneous and defective. This finding is illustrated from the first-principles study of hydrogen adsorption on ${\mathrm{MoS}}_{2}$ surfaces. A clear linear relationship between the energies of antibonding electron transfer and hydrogen adsorption is identified. Active sites of hydrogen evolution reaction on ${\mathrm{MoS}}_{2}$ are found to originate from the in-gap states induced by sulfur vacancies or edges. The emerging picture is general and suited for both metal and semiconductor surfaces. It also offers a physically different explanation for the well-known $d$-band model for hydrogen adsorption on transition-metal surfaces.

Honeycomb lattices (HLs) have been widely explored for realization of massless Dirac quasiparticles and intriguing topological properties in both the quantum and classical regimes, with elemental and artificial atoms as the corresponding building blocks. Here we provide a demonstration showing that when the fullerene molecules of ${\mathrm{C}}_{28}$ are used as prototypical building blocks, stable two-dimensional (2D) HLs can also be achieved, with the structural, electronic, and topological properties tuned via proper atom encapsulation. Specifically, whereas a closely packed structure is preferred for the ${\mathrm{C}}_{28}$ lattice, honeycomb structures with different spacial symmetries are energetically favored for both the Bi@${\mathrm{C}}_{28}$ and In@${\mathrm{C}}_{28}$ endohedral fullerenes. In particular, Bi@${\mathrm{C}}_{28}$-HL is revealed to be a quantum spin Hall insulator because of strong spin-orbit coupling effects in the $f$ molecular orbitals, while In@${\mathrm{C}}_{28}$-HL is a quantum valley Hall insulator resulting from mirror symmetry breaking. The present study offers superatom-based platforms for realizing quantum spin Hall and quantum valley Hall effects in 2D systems, with distinctly enhanced tunability and robustness against atomic-scale imperfections.

Recent years have witnessed the rapid increase in the application of deep learning in atomistic systems including both molecules and solid-state materials. The use of graphs and associated design of message passing strategies have enabled multiple deep learning frameworks to achieve reliable and efficient predictions of materials properties with a much smaller cost compared with first-principles atomistic simulations. In this review, we will focus on recent development of graph-based deep learning frameworks and their applications for both molecules and solid-state material systems. The history of the development of graph-based representations for molecules and crystals will be introduced. Essential learning processes defined by the so-called message passing will be reviewed, based on which the performance of different models will be compared. Furthermore, recent development of graph learning frameworks that incorporate material information beyond atom level will be introduced. Current challenges and future perspectives on this emerging field at the crossroad of material science, physics, chemistry, and computer science will be given, with an emphasize on how multiple tiers of material information can be organized and combined in a graph neural network setup.

Here, using solution based self-assembly, we contrast a three-dimensional co-crystal, where a [1]benzothieno[3,2-b][1]benzothiophene (BTBT) based donor and a tetracyanoquinodimethane (TCNQ) based acceptor leads to a mixed stacking sequence with pronounced intermolecular hybridization, with the donor–acceptor bilayer. Despite a comparable increase in conductivity, we observe a co-crystal with unique mechano-pyro-electric coupling properties.

For thermoelectrics, as well as many other applications of semiconductors, optimizing the carrier density is of great importance. This is normally done by atomic substitution. In this study, we present a result where systematic doping could arise from the least expected nanoparticles when composites are formed. We achieved this in a composite of PbSe and nanoparticle SiC. High carrier concentrations of up to 2 × 1019 cm−3 were achieved with SiC nanoparticle load up to 5 vol%. We believe this is due to interfaces that stabilized more defects than is allowed in bulk PbSe. Eventually, the nanoparticle-induced doping effect led to a decent thermoelectric performance with zT close to one at 800 K, comparable to optimized PbSe using conventional dopants. Our result indicates the good potential of this uncommon mechanism in some semiconductors for carrier concentration tuning where conventional doping has been found difficult.

The mechanical and electronic properties of transition metal dichalcogenide (TMD) monolayers corresponding to transition groups IV, VI, and X are explored under mechanical bending from first principles calculations using the strongly constrained and appropriately normed (SCAN) meta-GGA. SCAN provides an accurate description of the phase stability of the TMD monolayers. Our calculated lattice parameters and other structural parameters agree well with experiment. We find that bending stiffness (or flexural rigidity) increases as the transition metal group goes from IV to X to VI, with the exception of ${\mathrm{PdTe}}_{2}$. Variation in mechanical properties (local strain, physical thickness) and electronic properties (local charge density, band structure) with bending curvature is discussed. The local strain profile of these TMD monolayers under mechanical bending is highly nonuniform. The mechanical bending tunes not only the thickness of the TMD monolayers, but also the local charge distribution as well as the band structures, adding more functionalization options to these materials.

Unveiling both the presence and nature of point defects is one of the biggest challenges in condensed matter physics and materials science. Particularly in complex oxides, even a minute amount of unavoidable point defects could generate novel physical phenomena and functions, such as visible light emission and ferroelectricity, yet it remains elusive to clearly identify such point defects. Here, taking $\mathrm{SrTi}{\mathrm{O}}_{3}$ as a model system, we show that iterative feedback among atomic-layer- and stoichiometry-controlled thin-film epitaxy, hybrid density functional theory, and high-resolution cathodoluminescence spectroscopy allows for the identification of a functional cationic defect, the Ti antisite (${\mathrm{Ti}}_{\mathrm{Sr}}$) defect. Our cathodoluminescence measurements reveal sub-band-gap luminescence, whose spectral fine structures show excellent quantitative agreement, as well as a one-to-one correspondence, with the theoretically predicted optical transitions from intrinsic point defects such as ${\mathrm{Ti}}_{\mathrm{Sr}}$. Guided by the theory and spectroscopic results, we also control a cation stoichiometry, and it in turn results in good systematics in cathodoluminescence spectra. Not limited to the identification of ${\mathrm{Ti}}_{\mathrm{Sr}}$, this approach allows for more reliable, self-consistent defect study, and provides critical insight into a microscopic picture of point defects in complex oxides.

Combinatorial (photo)electrochemical studies of the (Ni–Mn)O_x system reveal a range of promising materials for oxygen evolution photoanodes. X-ray diffraction, quantum efficiency, and optical spectroscopy mapping reveal stable photoactivity of NiMnO_3 in alkaline conditions with photocurrent onset commensurate with its 1.9 eV direct band gap. The photoactivity increases upon mixture with 10–60% Ni_6MnO_8 providing an example of enhanced charge separation via heterojunction formation in mixed-phase thin film photoelectrodes. Density functional theory-based hybrid functional calculations of the band edge energies in this oxide reveal that a somewhat smaller than typical fraction of exact exchange is required to explain the favorable valence band alignment for water oxidation.

Solar fuel generators entail a high degree of materials integration, and efficient photoelectrocatalysis of the constituent reactions hinges upon the establishment of highly functional interfaces. The recent application of high throughput experimentation to interface discovery for solar fuels photoanodes has revealed several surprising and promising mixed-metal oxide coatings for BiVO4. Using sputter deposition of composition and thickness gradients on a uniform BiVO4 film, we systematically explore photoanodic performance as a function of the composition and loading of Fe–Ce oxide coatings. This combinatorial materials integration study not only enhances the performance of this new class of materials but also identifies CeO2 as a critical ingredient that merits detailed study. A heteroepitaxial CeO2(001)/BiVO4(010) interface is identified in which Bi and V remain fully coordinated to O such that no surface states are formed. Ab initio calculations of the integrated materials and inspection of the electronic structure reveals mechanisms by which CeO2 facilitates charge transport while mitigating deleterious recombination. The results support the observations that addition of Ce to BiVO4 coatings greatly enhances photoelectrocatalytic activity, providing an important strategy for developing a scalable solar fuels technology.

Using first-principles calculations, we predict that $\mathrm{TiRhAs}$, a previously synthesized compound, is a Dirac nodal line (DNL) semimetal. The DNL in this compound is found to be protected both by the combination of inversion and time-reversal symmetry, and by a reflection symmetry, in the absence of spin-orbit coupling (SOC). Our calculations show that band velocities associated with the nodal line have a high degree of directional anisotropy, with in-plane velocities ${v}_{\ensuremath{\perp}}$ perpendicular to the nodal line between $1.2--2.8\ifmmode\times\else\texttimes\fi{}{10}^{5}$ m/s. The crossings along the DNL are further found to exhibit a prominent and position-dependent tilt along directions perpendicular to the nodal line. We calculate ${\mathbb{Z}}_{2}$ indices based on parity eigenvalues at time-reversal invariant momenta and show that $\mathrm{TiRhAs}$ is topological. A tight-binding model fit from our first-principles calculations demonstrates the existence of two-dimensional drumhead surface states on the surface Brillouin zone. Based on the small gapping of the DNL upon inclusion of SOC and the clean Fermi surface free from trivial bands, $\mathrm{TiRhAs}$ is a promising candidate for further studies of the properties of topological semimetals.

Triply degenerate points (TDPs) in band structure of a crystal can generate novel TDP fermions without high-energy counterparts. Although identifying ideal TDP semimetals, which host clean TDP fermions around the Fermi level (${E}_{F}$) without coexisting with other quasiparticles, is critical to explore the intrinsic properties of this new fermion, it is still a big challenge and has not been achieved up to now. Here, we disclose an effective approach to search for ideal TDP semimetals via selective band crossing between antibonding $s$ and bonding $p$ orbitals along a line in the momentum space with ${C}_{3v}$ symmetry. Applying this approach, we have successfully identified the ${\mathrm{NaCu}}_{3}{\mathrm{Te}}_{2}$ family of compounds to be ideal TDP semimetals, where two, and only two, pairs of TDPs are located around the ${E}_{F}$. Moreover, we demonstrate a fundamental mechanism to modulate energy splitting between a pair of TDPs, and we illustrate the intrinsic features of TDP Fermi arcs in these ideal TDP semimetals.

© 2017 American Chemical Society. The solar photoelectrochemical generation of hydrogen and carbon-containing fuels comprises a critical energy technology for establishing sustainable energy resources. The photoanode, which is responsible for solar-driven oxygen evolution, has persistently limited technology advancement due to the lack of materials that exhibit both the requisite electronic properties and operational stability. Efforts to extend the lifetime of solar fuel devices increasingly focus on mitigating corrosion in the highly oxidizing oxygen evolution environment, motivating our development of a photoanode discovery pipeline that combines electronic structure calculations, Pourbaix stability screening, and high-throughput experiments. By applying the pipeline to ternary metal oxides containing manganese, we identify a promising class of corrosion-resistant materials and discover five oxygen evolution photoanodes, including the first demonstration of photoelectrocatalysis with Mn-based ternary oxides and the introduction of alkaline earth manganates as promising photoanodes for establishing a durable solar fuels technology.

The limited number of known low-band-gap photoelectrocatalytic materials poses a significant challenge for the generation of chemical fuels from sunlight. Using high-throughput ab initio theory with experiments in an integrated workflow, we find eight ternary vanadate oxide photoanodes in the target band-gap range (1.2–2.8 eV). Detailed analysis of these vanadate compounds reveals the key role of VO_4 structural motifs and electronic band-edge character in efficient photoanodes, initiating a genome for such materials and paving the way for a broadly applicable high-throughput-discovery and materials-by-design feedback loop. Considerably expanding the number of known photoelectrocatalysts for water oxidation, our study establishes ternary metal vanadates as a prolific class of photoanode materials for generation of chemical fuels from sunlight and demonstrates our high-throughput theory–experiment pipeline as a prolific approach to materials discovery.

We examine the effect of carrier localization due to random alloy fluctuations on the radiative and Auger recombination rates in InGaN quantum wells as a function of alloy composition, crystal orientation, carrier density, and temperature. Our results show that alloy fluctuations reduce individual transition matrix elements by the separate localization of electrons and holes, but this effect is overcompensated by the additional transitions enabled by translational symmetry breaking and the resulting lack of momentum conservation. Hence, we find that localization increases both radiative and Auger recombination rates, but that Auger recombination rates increase by one order of magnitude more than radiative rates. Furthermore, we demonstrate that localization has an overall detrimental effect on the efficiency-droop and green-gap problems of InGaN LEDs., Main text is 7 pages and includes 6 figures. Supplementary information is 7 pages and includes 2 figures and 2 tables

Materials with a negative Poisson's ratio, also known as auxetic materials, exhibit unusual and counterintuitive mechanical behavior - becoming fatter in cross-section when stretched. Such behavior is mostly attributed to some special reentrant or hinged geometric structures regardless the chemical composition and electronic structure of a material. Here, using first principles calculations, we report a new class of auxetic single-layer two-dimensional (2D) materials, i.e., the 1T-type monolayer crystals of groups 6-7 transition-metal dichalcogenides, MX$_2$ (M = Mo, W, Tc, Re; X = S, Se, Te). These materials have a crystal structure distinct from all other known auxetic materials. They exhibit an intrinsic in-plane negative Poisson's ratio, which is dominated by the electronic effects. We attribute the occurrence of such auxetic behavior to the strong coupling between the chalcogen p orbitals and the intermetal t$_{2g}$-bonding orbitals within the basic triangular pyramid structure unit. The unusual auxetic behavior in combination with other remarkable properties of monolayer 2D materials could lead to novel multi-functionalities., 21 pages, 4 figures

The formation energies of semiconductor point defects are nowadays predicted much more accurately by density functional theory than a decade ago, thanks to the use of modern hybrid functionals. The drawback is the high computational effort compared to the traditional local density approximation (LDA) or generalized gradient approximation (GGA). In this work, the authors trace back the main variations between the functionals to differences in the position of the bulk valence-band maximum, as well as in the reference energies for the chemical potential obtained with each functional. For point defects relevant for $p$-type GaN, these differences are accounted for by corrections, reducing the maximum disagreement between the different functionals from more than 2 eV to below 0.2 eV. The correction scheme should be useful for performing high-throughput calculations in cases where full hybrid functional calculations are prohibitively expensive.

© the Owner Societies 2016. Deployment of solar fuels technology requires photoanodes with long term stability, which can be accomplished using light absorbers that self-passivate under operational conditions. Several copper vanadates have been recently reported as promising photoanode materials, and their stability and self-passivation is demonstrated through a combination of Pourbaix calculations and combinatorial experimentation.

The surging demand for miniaturized compact devices has generated the need for new metal conductors with high current carrying ampacity, electric and thermal conductivity. Herein, we report carbon-metal conductors that exhibit a high breakdown current density (39% higher than copper) and electrical conductivity (e.g. 63% higher than that of copper at 363 K) in a broad temperature range. The mechanistic studies of thermal conductivity through first-principle modeling show that the multilayer graphene percolation networks efficiently decrease the electron-phonon coupling in the copper-graphene composites, even if phonon modes are activated at a high temperature. These results imply that the copper-based composites have the potential to be the next generation metal conductor with high electrical and thermal conductivity, as well as excellent current-carrying ampacity. More importantly, the developed composite can be deployed in the ink form, making it possible to be utilized by the microelectronic fabrication process.

In this short review, we introduce recent progress in the research field of data-driven material discovery and design for solar fuel generation. Construction of material databases under the materials genome initiative provides a great platform for material discovery and design by creating computational screening pipelines based on the materials’ descriptors. In the field of solar water splitting, data-driven computational discovery approach has been effective in making material predictions. When combined with synergistic and complimentary experimental efforts, high-throughput computations based on density functional theory showed great predictive power for accelerated discovery of inorganic compounds as functional materials for solar fuel generation. As an example, we introduce the theory–experiment joint discovery of a large set of metal oxide photoanode materials that have been theoretically predicted to be efficient candidates and soon verified by synergistic experimental fabrication and characterization processes. In the field of two-dimensional materials, the application of data-driven approach has realized the prediction of many promising candidates with suitable direct band gaps and optimal band edges for the generation of chemical fuels from sunlight, greatly expanding the number of theoretically predicted 2D photoelectrocatalysts that are awaiting experimental verification. We discuss the challenges for the continued discovery and design of novel bulk and 2D compounds for photocatalysis via a data-driven approach. At the end of this review, we provide a brief outlook for future material discoveries in the field of solar fuel generation.

Topological insulator (TI) states have been demonstrated in materials with narrow gap and large spin-orbit interactions (SOI). Here we demonstrate that nanoscale engineering can also give rise to a TI state, even in conventional semiconductors with sizable gap and small SOI. Based on advanced first-principles calculations combined with an effective low-energy k*p Hamiltonian, we show that the intrinsic polarization of materials can be utilized to simultaneously reduce the energy gap and enhance the SOI, driving the system to a TI state. The proposed system consists of ultrathin InN layers embedded into GaN, a layer structure that is experimentally achievable., 5 pages, 3 figures

Using first-principles methods and 8-band k·p simulations, we study the electronic structure of an ultrathin quantum-well system consisting of a single layer of InN inserted in GaN matrix. Experimental photoluminescence and electroluminescence emission peaks for such structures have been reported in the wavelength region between 380 to 450 nm. In contrast, our calculations show an energy difference between the electron and hole states around 2.17 eV (573 nm). Possible origins of the experimental light emission are examined. We suggest that the experimental emission may be due to recombination of electrons (holes) in GaN with holes (electrons) in the quantum well.