Final-state effects in X-ray spectra from transition metal oxides and silicates

Due to their chemical selectivity and the large amount of information that can be gained about the charge and coordination number (CN) of an element, X-ray absorption near-edge spectroscopy (XANES) and X-ray photoelectron spectroscopy (XPS) are routinely used to study metal centres in a variety of synthetic (e.g., alloys, ceramics, films) and natural (e.g., plants, soils) matrices. However, many competing effects influence spectral energies, and the ability to separate these effects is difficult. In particular, the effect of CN and the next-nearest neighbour (NNN) on XPS binding energies (BE) of inorganic solids have not been well-studied. In this work, the constituent elements of several industrially-relevant materials have been substituted and the resulting shifts in XPS and XANES spectral energies have been investigated, leading to a better understanding of the different effects that can cause these shifts. With increasing Zn content in SrFe(1-x)Zn(x)O(3-δ) (0 ≤ x ≤ 1), an oxygen-deficient perovskite-type structure, examination of Fe K- and Zn K-edge XANES spectra shows that greater oxygen deficiency (δ) lowers the transition-metal CN. Substitution of Fe by Zn results in shifts in the metal 2p XPS BEs that are much greater than the shifts observed in the corresponding L2,3-edge XANES absorption energies. As the number of electron-rich O2- anions surrounding the metal centres decreases, there is less electron density to screen the core-hole generated by XANES or XPS processes. Consequently, the poorly-screened core-hole exerts a stronger influence on the system, whose electrons relax to a greater extent. Further, O is electronegative compared to other atoms in the structure, and its tendency to tightly bind electrons restricts the ability of electrons from the material to relax around a core-hole on a metal centre. As the CN decreases, the magnitude of final-state relaxation around the core-hole increases, lowering the final-state energy and the observed BE. When the same core-electron is excited, this effect is more pronounced in XPS than in XANES, where the excited electron partially screens the core-hole. Investigations of (TiO2)x(SiO2)1-x (0 ≤ x ≤ 1), an amorphous metal-silicate, showed that the use of both hard (Ti K-edge) and soft (Ti L2,3-edge) X-rays provides a useful way to monitor changes in the bulk and surface, respectively. The bulk and surface regimes are critical for the applications of the amorphous transition-metal silicates, which are now being used as high-κ dielectric materials for use in semiconductors. Comparison of Ti K- and L2,3-edge spectra revealed that Ti atoms at the surface have a higher average CN than in the bulk, likely due to the presence of surface hydroxide and water groups that can coordinate to the Ti centres. The O K-edge, Ti L2,3-edge, and Si L2,3-edge XANES absorption energies showed little to no change with Ti content, while the O 1s, Ti 2p, and Si 2p XPS BEs were found to decrease significantly with increasing Ti content. As Ti replaces electronegative Si atoms, electrons in the material become less tightly bound and can relax to a greater extent around a core-hole. The larger degree of relaxation screens the core-hole more effectively in the final-state, lowering the final-state energy and all core-line BEs in these materials. Investigations of amorphous quaternary [(ZrO2)x(TiO2)y(SiO2)1-x-y (x + y = 0.20, 0.30)] and related ternary [(ZrO2)x(SiO2)1-x (0 ≤ x ≤ 1)] silicates found similar results. Namely, final-state relaxation increases with the amount of incorporated metal-oxide. The increase in final-state relaxation with total metal content has been confirmed empirically through analysis of the Auger parameter, which also increases with total metal content. These studies provide more examples that help us improve our understanding of the many influences that makes analysis of XPS spectra complicated, and highlight large changes in BE (>1 eV) that can occur without any changes in ground-state energies (e.g., oxidation state)

Design principles for oxide thermoelectric materials

Over 60% of the energy in the United States is wasted, most of it as heat. This amounts to staggering losses of natural and economic resources, and although some of this heat is Carnot heat that is unavoidable, even recovering a small fraction of the remaining waste heat would lead to economic and environmental benefits. Thermoelectric materials are a class of materials that can generate power from heat, but their widespread deployment has been limited because thermoelectric materials are currently inefficient, made from rare elements, and decompose at high temperatures when operated in air. Researchers have sought to develop oxide thermoelectric materials to overcome these shortfalls, but development of oxide materials is still relatively new, and has lacked guiding principles that have led to significant advances in traditional thermoelectric materials.The work presented here outlines the development of design principles for oxide thermoelectric materials, which involved the creation of a thermoelectric materials database, identification of the property space of interest, and the experimental preparation and characterization of materials in this property space. This work also highlights the development of machine learning models to create a materials recommendation engine. This recommendation engine has discovered a new class of thermoelectric materials with unexpected chemistry, and is being used to suggest other new material compositions likely to exhibit promising thermoelectric performance

Site-Net: Using global self-attention and real-space supercells to capture long-range interactions in crystal structures

Site-Net is a transformer architecture that models the periodic crystal structures of inorganic materials as a labelled point set of atoms and relies entirely on global self-attention and geometric information to guide learning. Site-Net processes standard crystallographic information files to generate a large real-space supercell, and the importance of interactions between all atomic sites is flexibly learned by the model for the prediction task presented. The attention mechanism is probed to reveal Site-Net can learn long-range interactions in crystal structures, and that specific attention heads become specialized to deal with primarily short- or long-range interactions. We perform a preliminary hyperparameter search and train Site-Net using a single graphics processing unit (GPU), and show Site-Net achieves state-of-the-art performance on a standard band gap regression task.Comment: 23 pages, 13 figure

Structural disorder, magnetism, and electrical and thermoelectric properties of pyrochlore Nd2Ru2O7

Polycrystalline Nd2Ru2O7 samples have been prepared and examined using a combination of structural, magnetic, and electrical and thermal transport studies. Analysis of synchrotron X-ray and neutron diffraction patterns suggests some site disorder on the A-site in the pyrochlore sublattice: Ru substitutes on the Nd-site up to 7.0(3)%, regardless of the different preparative conditions explored. Intrinsic magnetic and electrical transport properties have been measured. Ru 4d spins order antiferromagnetically at 143 K as seen both in susceptibility and specific heat, and there is a corresponding change in the electrical resistivity behaviour. A second antiferromagnetic ordering transition seen below 10 K is attributed to ordering of Nd 4f spins. Nd2Ru2O7 is an electrical insulator, and this behaviour is believed to be independent of the Ru-antisite disorder on the Nd site. The electrical properties of Nd2Ru2O7 are presented in the light of data published on all A2Ru2O7 pyrochlores, and we emphasize the special structural role that Bi3+ ions on the A-site play in driving metallic behaviour. High-temperature thermoelectric properties have also been measured. When considered in the context of known thermoelectric materials with useful figures-of-merit, it is clear that Nd2Ru2O7 has excessively high electrical resistivity which prevents it from being an effective thermoelectric. A method for screening candidate thermoelectrics is suggested.Comment: 19 pages, 10 figure

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials

Materials with high-dielectric constant easily polarize under external electric fields, allowing them to perform essential functions in many modern electronic devices. Their practical utility is determined by two conflicting properties: high dielectric constants tend to occur in materials with narrow band gaps, limiting the operating voltage before dielectric breakdown. We present a high-throughput workflow that combines element substitution, ML pre-screening, ab initio simulation and human expert intuition to efficiently explore the vast space of unknown materials for potential dielectrics, leading to the synthesis and characterization of two novel dielectric materials, CsTaTeO6 and Bi2Zr2O7. Our key idea is to deploy ML in a multi-objective optimization setting with concave Pareto front. While usually considered more challenging than single-objective optimization, we argue and show preliminary evidence that the $1/x$-correlation between band gap and permittivity in fact makes the task more amenable to ML methods by allowing separate models for band gap and permittivity to each operate in regions of good training support while still predicting materials of exceptional merit. To our knowledge, this is the first instance of successful ML-guided multi-objective materials optimization achieving experimental synthesis and characterization. CsTaTeO6 is a structure generated via element substitution not present in our reference data sources, thus exemplifying successful de-novo materials design. Meanwhile, we report the first high-purity synthesis and dielectric characterization of Bi2Zr2O7 with a band gap of 2.27 eV and a permittivity of 20.5, meeting all target metrics of our multi-objective search.Comment: 27 pages, 11 figures, 5 author

Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties

The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT) offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor [J. Carrete et al., Phys. Rev. X 4, 011019 (2014)], and experimental researchers generally do not directly use computation to drive their own synthesis efforts. To bridge this practical gap between experimental needs and computational tools, we report an open machine learning-based recommendation engine (http://thermoelectrics.citrination.com) for materials researchers that suggests promising new thermoelectric compositions based on pre-screening about 25 000 known materials and also evaluates the feasibility of user-designed compounds. We show this engine can identify interesting chemistries very different from known thermoelectrics. Specifically, we describe the experimental characterization of one example set of compounds derived from our engine, RE12Co5Bi (RE = Gd, Er), which exhibits surprising thermoelectric performance given its unprecedentedly high loading with metallic d and f block elements and warrants further investigation as a new thermoelectric material platform. We show that our engine predicts this family of materials to have low thermal and high electrical conductivities, but modest Seebeck coefficient, all of which are confirmed experimentally. We note that the engine also predicts materials that may simultaneously optimize all three properties entering into zT; we selected RE12Co5Bi for this study due to its interesting chemical composition and known facile synthesis.We thank the National Science Foundation for support of this research through NSF-DMR 1121053, as well as the Natural Sciences and Engineering Research Council of Canada (NSERC), and the DARPA SIMPLEX program N66001-15-C-4036. Additionally, this research made extensive use of shared experimental facilities of the Materials Research Laboratory: a NSF MRSEC, supported by NSF-DMR 1121053. MWG is thankful for support from NSERC through a Postgraduate Scholarship, support from the US Department of State through an International Fulbright Science & Technology Award, and support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska–Curie grant agreement No. 659764. BM and GJM are founders and significant shareholders in Citrine Informatics Inc

When do Anisotropic Magnetic Susceptibilities Lead to Large NMR Shifts? Exploring Particle Shape Effects in the Battery Electrode Material LiFePO4.

Materials used as electrodes in energy storage devices have been extensively studied with solid-state NMR spectroscopy. Due to the almost ubiquitous presence of transition metals, these systems are also often magnetic. While it is well known that the presence of anisotropic bulk magnetic susceptibility (ABMS) leads to broadening of resonances under MAS, we show that for mono-disperse and non-spherical particle morphologies, the ABMS can also lead to considerable shifts, which vary substantially as a function of particle shape. This, on one hand, complicates the interpretation of the NMR spectrum and the ability to compare the measured shift of different samples of the same system. On the other hand the ABMS shift provides a mechanism with which to derive the particle shape from the NMR spectrum. In this work, we present a methodology to model the ABMS shift, and relate it to the shape of the studied particles. The approach is tested on the $^7$Li NMR spectra of single crystals and powders of LiFePO$_4$. The results show that the ABMS shift can be a major contribution to the total NMR shift in systems with large magnetic anisotropies and small hyperfine shifts, $^7$Li shifts for typical LiFePO$_4$ morphologies varying by as much as 100 ppm. The results are generalised to demonstrate that the approach can be used as a means with which to probe the aspect ratio of particles. The work has implications for the analysis of NMR spectra of all materials with anisotropic magnetic susceptibilities, including diamagnetic materials such as graphite