RESUMO
The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 358 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. Our workflow can be applied to any other material property.
RESUMO
Magnetic materials have been applied in a large variety of technologies, from data storage to quantum devices. The development of two-dimensional (2D) materials has opened new arenas for magnetic compounds, even when classical theories discourage their examination. Here we propose a machine-learning-based strategy to predict and understand magnetic ordering in 2D materials. This strategy couples the prediction of the existence of magnetism in 2D materials using a random forest and the Shapley additive explanations method with material maps defined by atomic features predicting the magnetic ordering (ferromagnetic or antiferromagnetic). While the random forest model predicts magnetism with an accuracy of 86%, the material maps obtained by the sure independence screening and sparsifying method have an accuracy of â¼90% in predicting the magnetic ordering. Our model indicates that 3d transition metals, halides, and structural clusters with regular transition-metal sublattices have a positive contribution in the total weight deciding the existence of magnetism in 2D compounds. This behavior is associated with the competition between crystal field and exchange splitting. The machine learning model also indicates that the atomic spin orbit coupling (SOC) is a determinant feature for the identification of the patterns separating ferro- from antiferromagnetic order. The proposed strategy is used to identify novel 2D magnetic compounds that, together with the fundamental trends in the chemical and structural space, pave novel routes for experimental exploration.
RESUMO
The increasing interest and research on two-dimensional (2D) materials has not yet translated into a reality of diverse materials applications. To go beyond graphene and transition metal dichalcogenides for several applications, suitable candidates with desirable properties must be proposed. Here we use machine learning techniques to identify thermodynamically stable 2D materials, which is the first essential requirement for any application. According to the formation energy and energy above the convex hull, we classify materials as having low, medium, or high stability. The proposed approach enables the stability evaluation of novel 2D compounds for further detailed investigation of promising candidates, using only composition properties and structural symmetry, without the need for information about atomic positions. We demonstrate the usefulness of the model generating more than a thousand novel compounds, corroborating with DFT calculations the classification for five of these materials. To illustrate the applicability of the stable materials, we then perform a screening of electronic materials suitable for photoelectrocatalytic water splitting, identifying the potential candidate Sn2SeTe generated by our model, and also PbTe, both not yet reported for this application.
RESUMO
Three-dimensional topological insulators protected by both the time reversal (TR) and mirror symmetries were recently predicted and observed. Two-dimensional materials featuring this property and their potential for device applications have been less explored. We find that, in these systems, the spin polarization of edge states can be controlled with an external electric field breaking the mirror symmetry. This symmetry requires that the spin polarization is perpendicular to the mirror plane; therefore, the electric field induces spin-polarization components parallel to the mirror plane. Since this field preserves the TR topological protection, we propose a transistor model using the spin direction of protected edge states as a switch. In order to illustrate the generality of the proposed phenomena, we consider compounds protected by mirror planes parallel and perpendicular to the structure, e.g., Na_{3}Bi and half-functionalized (HF) hexagonal compounds, respectively. For this purpose, we first construct a tight-binding effective model for the Na_{3}Bi compound and predict that HF-honeycomb lattice materials are also dual topological insulators.
RESUMO
It is estimated that the city of São Paulo has over 2.5 million dogs and 560 thousand cats. These populations are irregularly distributed throughout the territory, making it difficult to appropriately allocate health services focused on these species. To reasonably allocate vaccination sites, it is necessary to identify social groups and their access to the referred service. Rabies in dogs and cats has been an important zoonotic health issue in São Paulo and the key component of rabies control is vaccination. The present study aims to introduce an approach to quantify the potential spatial accessibility to the vaccination sites of the 2009 campaign against rabies in the city of São Paulo and solve the overestimation associated with the classic methodology that applies buffer zones around vaccination sites based on Euclidean (straight-line) distance. To achieve this, a Gaussian-based two-step floating catchment area method with a travel-friction coefficient was adapted in a geographic information system environment, using distances along a street network based on Dijkstra's algorithm (short path method). The choice of the distance calculation method affected the results in terms of the population covered. In general, areas with low accessibility for both dogs and cats were observed, especially in densely populated areas. The eastern zone of the city had higher accessibility values compared with peripheral and central zones. The Gaussian-based two-step floating catchment method with a travel-friction coefficient was used to assess the overestimation of the straight-line distance method, which is the most widely used method for coverage analysis. We conclude that this approach has the potential to improve the efficiency of resource use when planning rabies control programs in large urban environments such as São Paulo. The findings emphasize the need for surveillance and intervention in isolated areas.