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

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

A data-driven review of thermoelectric materials: Performance and resource considerations

In this review, we describe the creation of a large database of thermoelectric materials prepared by abstracting information from over 100 publications. The database has over 18,000 data points from multiple classes of compounds, whose relevant properties have been measured at several temperatures. Appropriate visualization of the data immediately allows certain insights to be gained with regard to the property space of plausible thermoelectric materials. Of particular note is that any candidate material needs to display an electrical resistivity value that is close to 1 mΩcm at 300 K, i.e., samples should be significantly more conductive than the Mott minimum metallic conductivity. The Herfindahl-Hirschman index, a commonly accepted measure of market concentration, has been calculated from geological data (known elemental reserves) and geopolitical data (elemental production) for much of the periodic table. The visualization strategy employed here allows rapid sorting of thermoelectric compositions with respect to important issues of elemental scarcity and supply risk.Engineering and Applied Science

Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials

Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation