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Two-dimensional (2D) magnetic layered materials have revolutionized size dependent magnetism to manipulate spin-based devices. However, it has been challenging to artificially create 2D magnetic materials from three-dimensional (3D) crystal structures with a variety of material groups. Here, we present the dimensionality manipulation via cation exchange of a 3D Prussian blue analogue [RbMnFe(CN)

In the search for photoanode materials with band gaps suitable for utilization in solar fuel generation, approximately 1.2–2.8 eV, theory-guided experiments have identified a variety of materials that meet the band gap requirements and exhibit operational stability in harsh photoelectrochemical environments. In particular, M-V-O compounds (M is a transition metal or main group element) with VO₄ structural motifs were predicted to show a remarkably wide range of band energetics (>3 eV variation in the energy of valence band maximum) and characteristics, depending on the M and crystal structure, which is beyond the extent of electronic structured tuning observed in previously studied families of metal oxide photoanodes. While this finding guided experimental discovery of new photoanode materials, explicit experimental verification of the theoretical prediction of the tunable electronic structure of these materials has been lacking to date. In this study, we use X-ray photoelectron spectroscopy and Kelvin probe microscopy to experimentally investigate the electronic structure of M-V-O photoanodes, enabling comparison to theory on a common absolute energy scale. The results confirm the prediction that band edge energies of ternary vanadates vary significantly with metal cations. The valence band variation of approximately 1 eV observed here is larger than that reported in any analogous class of metal oxide semiconductors and demonstrates the promise of tuning the metal oxide electronic structure to enable efficient photoelectrocatalysis of the oxygen evolution reaction and beyond. Because midgap states can hamper realization of the high photovoltage sought by band edge tuning, we analyze the electronic contributions of oxygen vacancies for the representative photoanode V₄Cr₂O₁₃ to guide future research on the development of high-efficiency metal oxide photoanodes for solar fuel technology.

Magneto-ionics, real-time ionic control of magnetism in solid-state materials, promise ultralow-power memory, computing, and ultralow-field sensor technologies. The real-time ion intercalation is also the key state-of-charge feature in rechargeable batteries. Here, we report that the reversible lithiation/delithiation in molecular magneto-ionic material, the cathode in a rechargeable lithium-ion battery, accurately monitors its real-time state of charge through a dynamic tunability of magnetic ordering. The electrochemical and magnetic studies confirm that the structural vacancy and hydrogen-bonding networks enable reversible lithiation and delithiation in the magnetic cathode. Coupling with microwave-excited spin wave at a low frequency (0.35 GHz) and a magnetic field of 100 Oe, we reveal a fast and reliable built-in magneto-ionic sensor monitoring state of charge in rechargeable batteries. The findings shown herein promise an integration of molecular magneto-ionic cathode and rechargeable batteries for real-time monitoring of state of charge.

Mo2C and Ti3C2 MXenes were investigated as earth-abundant electrocatalyts for the CO2 reduction reaction (CO2RR). Mo2C and Ti3C2 exhibited faradaic efficiencies of 90% (250 mV overpotential) and 65% (650 mV overpotential), respectively, for the reduction of CO2 to CO in acetonitrile using an ionic liquid electrolyte. The use of ionic liquid 1-ethyl-2-methylimidazolium tetrafluoroborate as an electrolyte in organic solvent suppressed the competing hydrogen evolution reaction. Density functional theory (DFT) calculations suggested that the catalytic active sites are oxygen vacancy sites on both MXene surfaces. Also, a spontaneous dissociation of adsorbed COOH species to a water molecule and adsorbed CO on Mo2C promote the CO2RR.

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.

Being atomically thin and amenable to external controls, two-dimensional (2D) materials offer a new paradigm for the realization of patterned qubit fabrication and operation at room temperature for quantum information sciences applications. Here we show that the antisite defect in 2D transition metal dichalcogenides (TMDs) can provide a controllable solid-state spin qubit system. Using high-throughput atomistic simulations, we identify several neutral antisite defects in TMDs that lie deep in the bulk band gap and host a paramagnetic triplet ground state. Our in-depth analysis reveals the presence of optical transitions and triplet-singlet intersystem crossing processes for fingerprinting these defect qubits. As an illustrative example, we discuss the initialization and readout principles of an antisite qubit in WS2, which is expected to be stable against interlayer interactions in a multilayer structure for qubit isolation and protection in future qubit-based devices. Our study opens a new pathway for creating scalable, room-temperature spin qubits in 2D TMDs., Two-dimensional materials offer a promising platform for scalable solid-state spin qubit. Here, using high-throughput ab initio simulations, the authors identify suitable defect centers in monolayer group-VI transition metal dichalcogenides and assess their potential as qubits.

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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

Development of next-generation electronic devices for applications call for the discovery of quantum materials hosting novel electronic, magnetic, and topological properties. Traditional electronic structure methods require expensive computation time and memory consumption, thus a fast and accurate prediction model is desired with increasing importance. Representing the interactions among atomic orbitals in any material, a material Hamiltonian provides all the essential elements that control the structure-property correlations in inorganic compounds. Effective learning of material Hamiltonian by developing machine learning methodologies therefore offers a transformative approach to accelerate the discovery and design of quantum materials. With this motivation, we present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The information of each orbital includes the name, relative coordinates with respect to the center of super cell and the atom number, while the interaction between orbitals are represented by the Hamiltonian matrix. The results show that our model can get a promising prediction accuracy with cross-validation.

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...

This paper proposes a new multi-attribute decision making method based on Pythagorean fuzzy set and similarity measure. Firstly, the decision scheme is represented by Pythagorean fuzzy numbers, and then their sizes are ranked by the Pythagorean fuzzy number ranking method, so as to determine the positive ideal attribute value and the negative ideal attribute value. Then, the similarity between each decision-making scheme and the ideal attribute values is calculated respectively. Finally, the relative closeness of each scheme is calculated, and the optimal ranking is made according to its size. The validity and reliability of the proposed method are verified by case simulation.

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.

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. MoTe

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.

We present a first-principles study of MnNiO3, a promising oxygen-evolution photocatalyst. Using density functional theory with the PBE + U functional and the screened hybrid functional of Heyd, Scuseria, and Ernzerhof (HSE), we compute and analyze the ground-state geometry and electronic structure. We find that MnNiO3 is a ferrimagnetic semiconductor with an indirect band gap, consistent with experimental observations. We also predict that MnNiO3 has promising band edge positions relative to the vacuum, with potential to straddle the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) redox potentials in aqueous solution. A detailed analysis of the band structure and density of states provides a clear explanation for why MnNiO3 has appropriate electronic properties for OER. Furthermore, comprehensive calculations of its Pourbaix diagram suggest that MnNiO3 is stable in alkaline solution at potentials relevant for oxygen evolution.

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.