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The reaction between silylamido complexes of Cr(II), Fe(II), and Co(II) and IMes·2HF salt in the presence of IMes (IMes = 1,3-dimesitylimidazol-2-ylidene) led to isolation of Cr(IMes) 2 F 2 ( 2-Cr ), Fe(IMes) 2 F 2 ( 2-Fe ), and Co(IMes) 2 F 2 ( 2-Co ). X-ray structural studies revealed that 2-Cr adopts square planar geometry, while 2-Fe and 2-Co have distorted tetrahedral geometry. Magnetic susceptibility studies of 2-Cr , 2-Fe , and 2-Co were consistent with high-spin complexes, S = 2 for 2-Cr / 2-Fe and S = 3/2 for 2-Co . We demonstrated that fluoride can be successfully exchanged for cyanide and azide using trimethylsilyl cyanide and trimethylsilyl azide ( 3-Fe and 4-Fe ). DFT studies suggest that the preference of 2-Cr to adopt square planar geometry over tetrahedral is due to its d 4 metal center, where four electrons fill the lower-lying d-orbitals.

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 ∼90% 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.

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 Sn 2 SeTe generated by our model, and also PbTe, both not yet reported for this application.