We report on the electronic and optical properties of two theoretically predicted stable spinel compounds of the form ZnB 2 O 4 , where B = Ni or Cu; neither compound has been previously synthesized, so we compare them to the previously studied p-type ZnCo 2 O 4 spinel. These new materials exhibit spin polarization, which is useful for spintronics applications, and broad conductivity maxima near the valence band edge that indicate good p-type dopability. We show that 3d electrons on the octahedrally coordinated Zn atom fall deep within the valence band and do not contribute significantly to the electronic structure near the band edge of the material, while the O 2p and tetrahedrally coordinated B 3d electrons hybridize broadly in the shallow valence states, resulting in increasing curvature (i.e., decreased electron effective mass) of valence bands near the band edge. In particular, ZnCu 2 O 4 exhibits high electrical conductivities in the p-doping region near the valence band edge that, at σ = × − 2 10 S cm 4 1 , are twice the maximum found for ZnCo 2 O 4 , a previously synthesized compound in this class of materials. This material also exhibits ferromagnetism in all of its most stable structures, which makes it a good candidate for further study as a dilute magnetic semiconductor.

Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

<p>Global environmental challenges like climate change, pollution, and biodiversity loss are complex. To understand environmental patterns and processes and address these challenges, scientists require the observations of natural phenomena at various temporal and spatial scales and across many domains. The research infrastructures and scientific communities involved in these activities are often following their own data management practices which inevitably leads to a high degree of variability and incompatibility of approaches. Consequently, a variety of metadata standards and vocabularies have been proposed to describe observations and are actively used in different communities. However, this diversity in approaches now causes severe issues regarding the interoperability across datasets and hampers their exploitation as a common data source.</p><p>Projects like ENVRI-FAIR, FAIRsFAIR, FAIRplus are addressing this difficulty by working on the full integration of services across research infrastructures based on FAIR Guiding Principles supporting the EOSC vision towards an open research culture. Beyond these projects, we need collaboration and community consensus across domains to build a common framework for representing observable properties. The Research Data Alliance InteroperAble Descriptions of Observable Property Terminology Working Group (RDA I-ADOPT WG) was formed in October 2019 to address this need. Its membership covers an international representation of terminology users and terminology providers, including terminology developers, scientists, and data centre managers. The group’s overall objective is to deliver a common interoperability framework for observable property variables within its 18-month work plan. Starting with the collection of user stories from research scientists, terminology managers, and data managers or aggregators, we drafted a set of technical and content-related requirements. A survey of terminology resources and annotation practices provided us with information about almost one hundred terminologies, a subset of which was then analysed to identify existing conceptualisation practices, commonalities, gaps, and overlaps. This was then used to derive a conceptual framework to support their alignment. </p><p>In this presentation, we will introduce the I-ADOPT Interoperability Framework highlighting its semantic components. These represent the building blocks for specific ontology design patterns addressing different use cases and varying degrees of complexity in describing observed properties. We will demonstrate the proposed design patterns using a number of essential climate and essential biodiversity variables. We will also show examples of how the I-ADOPT framework will support interoperability between existing representations. This work will provide the semantic foundation for the development of more user-friendly data annotation tools capable of suggesting appropriate FAIR terminologies for observable properties.</p>

This work presents theoretical calculations of the electrical transport properties of the Ag, Au, and La fractionally filled bulk skutterudites: CoSb 3 , CoAs 3 , and CoP 3 . Density functional theory (DFT), along with projector augmented wave (PAW) potentials, was used to calculate bulk band structures and partial density of states. The Seebeck coefficient (S), electrical conductivity (σ), and power factor (S 2 σ) were calculated as a function of temperature and filling fraction using Finally, we recommend future directions for improving the thermoelectric figure of merit of these materials.