Ferroelectric Materials for Solar Energy Conversion: Photoferroics Revisited

The application of ferroelectric materials (i.e. solids that exhibit spontaneous electric polarisation) in solar cells has a long and controversial history. This includes the first observations of the anomalous photovoltaic effect (APE) and the bulk photovoltaic effect (BPE). The recent successful application of inorganic and hybrid perovskite structured materials (e.g. BiFeO3, CsSnI3, CH3NH3PbI3) in solar cells emphasises that polar semiconductors can be used in conventional photovoltaic architectures. We review developments in this field, with a particular emphasis on the materials known to display the APE/BPE (e.g. ZnS, CdTe, SbSI), and the theoretical explanation. Critical analysis is complemented with first-principles calculation of the underlying electronic structure. In addition to discussing the implications of a ferroelectric absorber layer, and the solid state theory of polarisation (Berry phase analysis), design principles and opportunities for high-efficiency ferroelectric photovoltaics are presented

Electronic chemical potentials of porous metal-organic frameworks

The binding energy of an electron in a material is a fundamental characteristic, which determines a wealth of important chemical and physical properties. For metal-organic frameworks this quantity is hitherto unknown. We present a general approach for determining the vacuum level of porous metal-organic frameworks and apply it to obtain the first ionisation energy for six prototype materials including zeolitic, covalent and ionic frameworks. This approach for valence band alignment can explain observations relating to the electrochemical, optical and electrical properties of porous frameworks

Ultra-thin oxide films for band engineering: design principles and numerical experiments

AbstractThe alignment of band energies between conductive oxides and semiconductors is crucial for the further development of oxide contacting layers in electronic devices. The growth of ultra thin films on the surface of an oxide material can be used to introduce a dipole moment at that surface due to charge differences. The dipole, in turn, alters the electrostatic potential — and hence the band energies — in the substrate oxide. We demonstrate the fundamental limits for the application of thin-films in this context, applying analytical and numerical simulations, that bridge continuum and atomistic. The simulations highlight the different parameters that can affect the band energy shifting potential of a given thin-film layer, taking the examples of MgO and SnO2. In particular we assess the effect of formal charge, layer orientation, layer thickness and surface coverage, with respect to their effect on the electrostatic potential. The results establish some design principles, important for further development and application of thin-films for band energy engineering in transparent conductive oxide materials

Quantitative spectroscopy of extreme helium stars - Model atmospheres and a non-LTE abundance analysis of BD+10∘^\circ2179?

Extreme helium stars (EHe stars) are hydrogen-deficient supergiants of spectral type A and B. They are believed to result from mergers in double degenerate systems. In this paper we present a detailed quantitative non-LTE spectral analysis for BD+10$^\circ$2179, a prototype of this rare class of stars, using UVES and FEROS spectra covering the range from $\sim$3100 to 10 000 {\AA}. Atmosphere model computations were improved in two ways. First, since the UV metal line blanketing has a strong impact on the temperature-density stratification, we used the Atlas12 code. Additionally, We tested Atlas12 against the benchmark code Sterne3, and found only small differences in the temperature and density stratifications, and good agreement with the spectral energy distributions. Second, 12 chemical species were treated in non-LTE. Pronounced non-LTE effects occur in individual spectral lines but, for the majority, the effects are moderate to small. The spectroscopic parameters give $T_\mathrm{eff}$ = 17 300$\pm$300 K and $\log g$ = 2.80$\pm$0.10, and an evolutionary mass of 0.55$\pm$0.05 $M_\odot$. The star is thus slightly hotter, more compact and less massive than found in previous studies. The kinematic properties imply a thick-disk membership, which is consistent with the metallicity $[$Fe/H$]\approx-1$ and $\alpha$-enhancement. The refined light-element abundances are consistent with the white dwarf merger scenario. We further discuss the observed helium spectrum in an appendix, detecting dipole-allowed transitions from about 150 multiplets plus the most comprehensive set of known/predicted isolated forbidden components to date. Moreover, a so far unreported series of pronounced forbidden He I components is detected in the optical-UV.Comment: Accepted for publication in MNRAS, 26 pages, 19 Figure

Crystal Structure Generation with Autoregressive Large Language Modeling

The generation of plausible crystal structures is often an important step in the computational prediction of crystal structures from composition. Here, we introduce a methodology for crystal structure generation involving autoregressive large language modeling of the Crystallographic Information File (CIF) format. Our model, CrystaLLM, is trained on a comprehensive dataset of millions of CIF files, and is capable of reliably generating correct CIF syntax and plausible crystal structures for many classes of inorganic compounds. Moreover, we provide general and open access to the model by deploying it as a web application, available to anyone over the internet. Our results indicate that the model promises to be a reliable and efficient tool for both crystallography and materials informatics

Predicting Thermoelectric Transport Properties from Composition with Attention-based Deep Learning

Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To enable deployment on global scales, new classes of effective thermoelectrics are thus required. $\textit{Ab initio}$ models of transport properties can help in the design of new thermoelectrics, but they are still too computationally expensive to be solely relied upon for high-throughput screening in the vast chemical space of all possible candidates. Here, we use models constructed with modern machine learning techniques to scan very large areas of inorganic materials space for novel thermoelectrics, using composition as an input. We employ an attention-based deep learning model, trained on data derived from $\textit{ab initio}$ calculations, to predict a material's Seebeck coefficient, electrical conductivity, and power factor over a range of temperatures and $\textit{n}$- or $\textit{p}$-type doping levels, with surprisingly good performance given the simplicity of the input, and with significantly lower computational cost. The results of applying the model to a space of known and hypothetical binary and ternary selenides reveal several materials that may represent promising thermoelectrics. Our study establishes a protocol for composition-based prediction of thermoelectric behaviour that can be easily enhanced as more accurate theoretical or experimental databases become available

Ultra-thin oxide films for band engineering: design principles and numerical experiments ☆

The alignment of band energies between conductive oxides and semiconductors is crucial for the further development of oxide contacting layers in electronic devices. The growth of ultra thin films on the surface of an oxide material can be used to introduce a dipole moment at that surface due to charge differences. The dipole, in turn, alters the electrostatic potential -and hence the band energies -in the substrate oxide. We demonstrate the fundamental limits for the application of thin-films in this context, applying analytical and numerical simulations, that bridge continuum and atomistic. The simulations highlight the different parameters that can affect the band energy shifting potential of a given thin-film layer, taking the examples of MgO and SnO 2 . In particular we assess the effect of formal charge, layer orientation, layer thickness and surface coverage, with respect to their effect on the electrostatic potential. The results establish some design principles, important for further development and application of thin-films for band energy engineering in transparent conductive oxide materials