propnet: A Knowledge Graph for Materials Science

Discovering the ideal material for a new application involves determining its numerous properties, such as electronic, mechanical, or thermodynamic, to match those of its desired application. The rise of high-throughput computation has meant that large databases of material properties are now accessible to scientists. However, these databases contain far more information than might appear at first glance, since many relationships exist in the materials science literature to derive, or at least approximate, additional properties. propnet is a new computational framework designed to help scientists to automatically calculate additional information from their datasets. It does this by constructing a network graph of relationships between different materials properties and traversing this graph. Initially, propnet contains a catalog of over 100 property relationships but the hope is for this to expand significantly in the future, and contributions from the community are welcomed

optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs

In recent decades, improvements in algorithms, hardware, and theory have enabled crystalline materials to be studied computationally at the atomistic level with great accuracy and speed. To enable dissemination, reproducibility, and reuse, many digital crystal structure databases have been created and curated, ready for comparison with existing infrastructure that stores structural characterizations (e.g., diffraction) of real crystals. Each database will typically have a bespoke, stateless, web-based Application Programming Interface (API); users can submit a query via specially-crafted URLs. Such esoteric and specialized APIs incur maintenance and usability costs upon both the data providers and consumers, who may not be software specialists. The OPTIMADE API specification (Andersen et al., 2020, 2021), released in July 2020, aimed to reduce these costs by designing a common API for use across a consortium of collaborating materials databases and beyond. Whilst based on the robust JSON:API standard (Katz et al., 2015), the OPTIMADE API specification presents several domain-specific features and re- quirements that can be tricky to implement for non-specialist teams. The repository presented here, optimade-python-tools, provides a modular reference server implementation and a set of associated tools to accelerate the development process for data providers, toolmakers and end-user

Shared Metadata for Data-Centric Materials Science

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on "Shared Metadata and Data Formats for Big-Data Driven Materials Science". We start from an operative definition of metadata, and what features a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them

Measurements to Elucidate the Mechanism of Thermal and Radiation Enhanced Diffusion of Cesium, Europium, and Strontium in Silicon Carbide.

Containment of fission products (FP) within the TRISO fuel particle is critical to the success of the very high temperature reactor (VHTR). Over sixty years of experience developing and testing this fuel has yet to identify the mechanism by which several key fission products (cesium, europium, and strontium) escape through intact SiC at temperatures between 900C and 1,300C. A novel diffusion couple was developed that was successful in making the first measurements of fission product diffusion in SiC. This design allows for the isolation of thermal diffusion and investigation of radiation enhanced diffusion using ion irradiation as a simulant for neutron radiation damage. The thermal and radiation enhanced diffusion of cesium, europium, and strontium were measured between 900C and 1,300C. The ion irradiation significantly enhanced the diffusion of all three fission products with enhancement factors ranging from 100x to 1E7x over thermal diffusion. All three fission products exhibits mixed diffusion kinetics between 900C and 1,300C under purely thermal conditions, and between 900C and 1,100C under ion irradiation. This indicates that both bulk and grain boundary diffusion are active mechanisms for fission product release. A defect reaction model indicates that fission product diffusion can occur on both the silicon or carbon sub-lattices. Comparison of cesium diffusion with the literature suggests that the best quality TRISO fuel should exhibit minimal cesium release and that cesium release is a good indicator of TRISO fuel failure.PhDNuclear Engineering and Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120786/1/shyamd_1.pd

Effective Local Geometry Descriptor for29Si NMR Q4Anisotropy

The nuclear shielding anisotropy, ζ, is a useful nuclear magnetic resonance (NMR) shielding tensor parameter in describing the extent of electron cloud distortion about an atom. Despite the advantages afforded by NMR in structural characterization, the relationship between ζ and local structure of an atom in high-symmetry environments, such as Si-Q4sites, is poorly understood. Here, we use a data-driven approach combining random forest feature ranking and the Sure Independence Screening and Sparsifying Operator (SISSO) approach to derive a simple and accurate geometric descriptor for ζ with a root-mean-squared prediction error of 6.77 ppm and anR2of 0.761. We then apply this descriptor to describe the local geometric distortion of zeolites Sigma-2 and silica-ZSM-5 whose chemical shift anisotropy tensor has been reported. We envision that this geometric descriptor will allow for structural description and refinement in previously difficult-to-describe materials

Thermodynamic limit for synthesis of metastable inorganic materials.

Realizing the growing number of possible or hypothesized metastable crystalline materials is extremely challenging. There is no rigorous metric to identify which compounds can or cannot be synthesized. We present a thermodynamic upper limit on the energy scale, above which the laboratory synthesis of a polymorph is highly unlikely. The limit is defined on the basis of the amorphous state, and we validate its utility by effectively classifying more than 700 polymorphs in 41 common inorganic material systems in the Materials Project for synthesizability. The amorphous limit is highly chemistry-dependent and is found to be in complete agreement with our knowledge of existing polymorphs in these 41 systems, whether made by the nature or in a laboratory. Quantifying the limits of metastability for realizable compounds, the approach is expected to find major applications in materials discovery

An improved symmetry-based approach to reciprocal space path selection in band structure calculations

Band structures for electrons, phonons, and other quasiparticles are often an important aspect of describing the physical properties of periodic solids. Most commonly, energy bands are computed along a one-dimensional path of high-symmetry points and line segments in reciprocal space (the “k-path”), which are assumed to pass through important features of the dispersion landscape. However, existing methods for choosing this path rely on tabulated lists of high-symmetry points and line segments in the first Brillouin zone, determined using different symmetry criteria and unit cell conventions. Here we present a new “on-the-fly” symmetry-based approach to obtaining paths in reciprocal space that attempts to address the previous limitations of these conventions. Given a unit cell of a magnetic or nonmagnetic periodic solid, the site symmetry groups of points and line segments in the irreducible Brillouin zone are obtained from the total space group. The elements in these groups are used alongside general and maximally inclusive high-symmetry criteria to choose segments for the final k-path. A smooth path connecting each segment is obtained using graph theory. This new framework not only allows for increased flexibility and user convenience but also identifies notable overlooked features in certain electronic band structures. In addition, a more intelligent and efficient method for analyzing magnetic materials is also enabled through proper accommodation of magnetic symmetry

Evaluation of thermodynamic equations of state across chemistry and structure in the materials project

Thermodynamic equations of state (EOS) for crystalline solids describe material behaviors under changes in pressure, volume, entropy and temperature, making them fundamental to scientific research in a wide range of fields including geophysics, energy storage and development of novel materials. Despite over a century of theoretical development and experimental testing of energy–volume (E–V) EOS for solids, there is still a lack of consensus with regard to which equation is indeed optimal, as well as to what metric is most appropriate for making this judgment. In this study, several metrics were used to evaluate quality of fit for 8 different EOS across 87 elements and over 100 compounds which appear in the literature. Our findings do not indicate a clear “best” EOS, but we identify three which consistently perform well relative to the rest of the set. Furthermore, we find that for the aggregate data set, the RMSrD is not strongly correlated with the nature of the compound, e.g., whether it is a metal, insulator, or semiconductor, nor the bulk modulus for any of the EOS, indicating that a single equation can be used across a broad range of classes of materials

A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis.

Accelerated inorganic synthesis remains a significant challenge in the search for novel, functional materials. Many of the principles which enable "synthesis by design" in synthetic organic chemistry do not exist in solid-state chemistry, despite the availability of extensive computed/experimental thermochemistry data. In this work, we present a chemical reaction network model for solid-state synthesis constructed from available thermochemistry data and devise a computationally tractable approach for suggesting likely reaction pathways via the application of pathfinding algorithms and linear combination of lowest-cost paths in the network. We demonstrate initial success of the network in predicting complex reaction pathways comparable to those reported in the literature for YMnO3, Y2Mn2O7, Fe2SiS4, and YBa2Cu3O6.5. The reaction network presents opportunities for enabling reaction pathway prediction, rapid iteration between experimental/theoretical results, and ultimately, control of the synthesis of solid-state materials

Evaluation of thermodynamic equations of state across chemistry and structure in the materials project

Thermodynamic equations of state (EOS) for crystalline solids describe material behaviors under changes in pressure, volume, entropy and temperature, making them fundamental to scientific research in a wide range of fields including geophysics, energy storage and development of novel materials. Despite over a century of theoretical development and experimental testing of energy–volume (E–V) EOS for solids, there is still a lack of consensus with regard to which equation is indeed optimal, as well as to what metric is most appropriate for making this judgment. In this study, several metrics were used to evaluate quality of fit for 8 different EOS across 87 elements and over 100 compounds which appear in the literature. Our findings do not indicate a clear “best” EOS, but we identify three which consistently perform well relative to the rest of the set. Furthermore, we find that for the aggregate data set, the RMSrD is not strongly correlated with the nature of the compound, e.g., whether it is a metal, insulator, or semiconductor, nor the bulk modulus for any of the EOS, indicating that a single equation can be used across a broad range of classes of materials