Publications by authors named "Lany S"

To synthetically target a specific material with select performance, the underlying relationship between structure and function must be understood. For targeting magnetic properties, such understanding is underdeveloped for a relatively new class of layered hexagonal perovskites, the 12R-BaMnO family. Here, we perform a detailed magnetostructural study of the layered hexagonal perovskite materials 12R-BaMnO, where = diamagnetic Ce or paramagnetic ≈ 1/2 Pr.

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We investigate the synthesis of antiperovskite "MnAlN" using the published synthesis procedure, as well as several new reaction pathways. In each case, only a combination of antiperovskite MnN and MnAl or precursors is obtained. The identity of the obtained antiperovskite phase is unambiguously determined to be MnN via synchrotron powder X-ray diffraction (SPXRD), X-ray absorption spectroscopy (XAS), and magnetometry.

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Mixing multiple cations can result in a significant configurational entropy, offer a new compositional space with vast tunability, and introduce new computational challenges. For applications such as the two-step solar thermochemical hydrogen (STCH) generation techniques, we demonstrate that using density functional theory (DFT) combined with Metropolis Monte Carlo method (DFT-MC) can efficiently sample the possible cation configurations in compositionally complex perovskite oxide (CCPO) materials, with (LaSr)(MnFeCoAl)O as an example. In the presence of oxygen vacancies (), DFT-MC simulations reveal a significant increase of the local site preference of the cations (short-range ordering), compared to a more random mixing without .

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The van't Hoff method is a standard approach for determining reaction enthalpies and entropies, e.g., in the thermochemical reduction of oxides, which is an important process for solar thermochemical fuels and numerous other applications.

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We present a graph neural network approach that fully automates the prediction of defect formation enthalpies for any crystallographic site from the ideal crystal structure, without the need to create defected atomic structure models as input. Here we used density functional theory reference data for vacancy defects in oxides, to train a defect graph neural network (dGNN) model that replaces the density functional theory supercell relaxations otherwise required for each symmetrically unique crystal site. Interfaced with thermodynamic calculations of reduction entropies and associated free energies, the dGNN model is applied to the screening of oxides in the Materials Project database, connecting the zero-kelvin defect enthalpies to high-temperature process conditions relevant for solar thermochemical hydrogen production and other energy applications.

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The resurgence of interest in a hydrogen economy and the development of hydrogen-related technologies has initiated numerous research and development efforts aimed at making the generation, storage, and transportation of hydrogen more efficient and affordable. Solar thermochemical hydrogen production (STCH) is a process that potentially exhibits numerous benefits such as high reaction efficiencies, tunable thermodynamics, and continued performance over extended cycling. Although CeO has been the de facto standard STCH material for many years, more recently 12R-BaCeMnO (BCM) has demonstrated enhanced hydrogen production at intermediate H/HO conditions compared to CeO, making it a contender for large-scale hydrogen production.

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Materials design from first principles enables exploration of uncharted chemical spaces. Extensive computational searches have been performed for mixed-cation ternary compounds, but mixed-anion systems are gaining increased interest as well. Central to computational discovery is the crystal structure prediction, where the trade-off between reliance on prototype structures and size limitations of unconstrained sampling has to be navigated.

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The approaching end of Moore's law scaling has significantly accelerated multiple fields of research including neuromorphic-, quantum-, and photonic computing, each of which possesses unique benefits unobtained through conventional binary computers. One of the most compelling arguments for neuromorphic computing systems is power consumption, noting that computations made in the human brain are approximately 10times more efficient than conventional CMOS logic. This review article focuses on the materials science and physical mechanisms found in metal chalcogenides that are currently being explored for use in neuromorphic applications.

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Nitride materials feature strong chemical bonding character that leads to unique crystal structures, but many ternary nitride chemical spaces remain experimentally unexplored. The search for previously undiscovered ternary nitrides is also an opportunity to explore unique materials properties, such as transitions between cation-ordered and -disordered structures, as well as to identify candidate materials for optoelectronic applications. Here, we present a comprehensive experimental study of MgSnN, an emerging II-IV-N compound, for the first time mapping phase composition and crystal structure, and examining its optoelectronic properties computationally and experimentally.

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We used high-throughput experimental screening methods to unveil the physical and chemical properties of Mn Zn O wurtzite alloys and identify their appropriate composition for effective water splitting application. The Mn Zn O thin films were synthesized using combinatorial pulsed laser deposition, permitting for characterization of a wide range of compositions with varying from 0 to 1. The solubility limit of ZnO in MnO was determined using the disappearing phase method from X-ray diffraction and X-ray fluorescence data and found to increase with decreasing substrate temperature due to kinetic limitations of the thin-film growth at relatively low temperature.

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Inorganic nitrides with wurtzite crystal structures are well-known semiconductors used in optical and electronic devices. In contrast, rocksalt-structured nitrides are known for their superconducting and refractory properties. Breaking this dichotomy, here we report ternary nitride semiconductors with rocksalt crystal structures, remarkable electronic properties, and the general chemical formula Mg N ( = Ti, Zr, Hf, Nb).

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Exploratory synthesis in new chemical spaces is the essence of solid-state chemistry. However, uncharted chemical spaces can be difficult to navigate, especially when materials synthesis is challenging. Nitrides represent one such space, where stringent synthesis constraints have limited the exploration of this important class of functional materials.

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The opportunity for enhanced functional properties in semiconductor solid solutions has attracted vast scientific interest for a variety of novel applications. However, the functional versatility originating from the additional degrees of freedom due to atomic composition and ordering comes along with new challenges in characterization and modeling. Developing predictive synthesis-structure-property relationships is prerequisite for effective materials design strategies.

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Carrier doping of quantum spin liquids is a long-proposed route to the emergence of high-temperature superconductivity. Electrochemical intercalation in kagome hydroxyl halide materials shows that samples remain insulating across a wide range of electron counts. Here we demonstrate through first-principles density-functional calculations, corrected for self-interaction, the mechanism by which electrons remain localized in various Zn-Cu hydroxyl halides, independent of the chemical identity of the dopant-the formation of polaronic states with attendant lattice displacements and a dramatic narrowing of bandwidth upon electron addition.

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The Gibbs energy, G, determines the equilibrium conditions of chemical reactions and materials stability. Despite this fundamental and ubiquitous role, G has been tabulated for only a small fraction of known inorganic compounds, impeding a comprehensive perspective on the effects of temperature and composition on materials stability and synthesizability. Here, we use the SISSO (sure independence screening and sparsifying operator) approach to identify a simple and accurate descriptor to predict G for stoichiometric inorganic compounds with ~50 meV atom (~1 kcal mol) resolution, and with minimal computational cost, for temperatures ranging from 300-1800 K.

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The ability of a material to adopt multiple structures, known as polymorphism, is a fascinating natural phenomenon. Various polymorphs with unusual properties are routinely synthesized by compression under positive pressure. However, changing a material's structure by applying tension under negative pressure is much more difficult.

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We report on the theoretical prediction and experimental realization of new ternary zinc molybdenum nitride compounds. We used theory to identify previously unknown ternary compounds in the Zn-Mo-N systems, ZnMoN and ZnMoN, and to analyze their bonding environment. Experiments show that Zn-Mo-N alloys can form in broad composition range from ZnMoN to ZnMoN in the wurtzite-derived structure, accommodating very large off-stoichiometry.

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The ideal material for solar thermochemical water splitting, which has yet to be discovered, must satisfy stringent conditions for the free energy of reduction, including, in particular, a sufficiently large positive contribution from the solid-state entropy. By inverting the commonly used relationship between defect formation energy and defect concentration, it is shown here that charged defect formation causes a large electronic entropy contribution manifesting itself as the temperature dependence of the Fermi level. This result is a general feature of charged defect formation and motivates new materials design principles for solar thermochemical hydrogen production.

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Cu(In,Ga)Se (CIGS) is presently the most efficient thin-film photovoltaic technology with efficiencies exceeding 22%. An important factor impacting the efficiency is metastability, where material changes occur over timescales of up to weeks during light exposure. A previously proposed (V -V ) divacancy model presents a widely accepted explanation.

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Two-dimensional (2D) materials offer many key advantages to innovative applications, such as spintronics and quantum information processing. Theoretical computations have accelerated 2D materials design. In this issue of ACS Nano, Kumar et al.

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Structure and composition control the behavior of materials. Isostructural alloying is historically an extremely successful approach for tuning materials properties, but it is often limited by binodal and spinodal decomposition, which correspond to the thermodynamic solubility limit and the stability against composition fluctuations, respectively. We show that heterostructural alloys can exhibit a markedly increased range of metastable alloy compositions between the binodal and spinodal lines, thereby opening up a vast phase space for novel homogeneous single-phase alloys.

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Semiconductor materials that can be doped both n-type and p-type are desirable for diode-based applications and transistor technology. Copper nitride (CuN) is a metastable semiconductor with a solar-relevant bandgap that has been reported to exhibit bipolar doping behavior. However, deeper understanding and better control of the mechanism behind this behavior in CuN is currently lacking in the literature.

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Recent advances in theoretical structure prediction methods and high-throughput computational techniques are revolutionizing experimental discovery of the thermodynamically stable inorganic materials. Metastable materials represent a new frontier for these studies, since even simple binary non-ground state compounds of common elements may be awaiting discovery. However, there are significant research challenges related to non-equilibrium thin film synthesis and crystal structure predictions, such as small strained crystals in the experimental samples and energy minimization based theoretical algorithms.

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We use a high signal-to-noise X-ray photoelectron spectrum of bulk PbS, GW calculations, and a model assuming parabolic bands to unravel the various X-ray and ultraviolet photoelectron spectral features of bulk PbS as well as determine how to best analyze the valence band region of PbS quantum dot (QD) films. X-ray and ultraviolet photoelectron spectroscopy (XPS and UPS) are commonly used to probe the difference between the Fermi level and valence band maximum (VBM) for crystalline and thin-film semiconductors. However, we find that when the standard XPS/UPS analysis is used for PbS, the results are often unrealistic due to the low density of states at the VBM.

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The design of thermoelectric materials often involves the integration of point defects (alloying) as a route to reduce the lattice thermal conductivity. Classically, the point defect scattering strength follows from simple considerations such as mass contrast and the presence of induced strain fields (e.g.

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