High-throughput computation and structure prototype analysis for two-dimensional ferromagnetic materials

We perform high-throughput first-principles computations to search the high Curie temperature ($T_{\rm C}$) two-dimensional ferromagnetic (2DFM) materials. We identify 79 2DFM materials and calculate their $T_{\rm C}$, in which Co$_2$F$_2$ has the highest $T_{\rm C}$=541K, well above the room temperature. The 79 2DFM materials are classified into different structural prototypes according to their structural similarity. We perform sure independence screening and sparsifying operator (SISSO) analysis to explore the relation between $T_{\rm C}$ and the material structures. The results suggest that the 2DFM materials with shorter distance between the magnetic atoms, larger local magnetic moments and more neighboring magnetic atoms are more likely to have higher $T_{\rm C}$

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 the features that 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

Shared metadata for data-centric materials science

The expansive production of data in materials science, their widespread sharing andrepurposing requires educated support and stewardship. In order to ensure that this needhelps rather than hinders scientific work, the implementation of the FAIR-data principles(Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, thewider materials-science community ought to agree on the strategies to tackle the challengesthat are specific to its data, both from computations and experiments. In this paper, wepresent the result of the discussions held at the workshop on “Shared Metadata and DataFormats for Big-Data Driven Materials Science”. We start from an operative definition ofmetadata, and the features that a FAIR-compliant metadata schema should have. Wewill mainly focus on computational materials-science data and propose a constructiveapproach for the FAIRification of the (meta)data related to ground-state and excited-statescalculations, potential-energy sampling, and generalized workflows. Finally, challenges withthe FAIRification of experimental (meta)data and materials-science ontologies are presentedtogether with an outlook of how to meet them