The feasibility of mixed-cation mixed-halogen perovskites of formula AxA’1−xPbXyX’zX”3−y−z is analyzed from the perspective of structural stability, opto-electronic properties and possible degradation mechanisms. Using density functional theory (DFT) calculations aided by machine-learning (ML) methods, the structurally stable compositions are further evaluated for the highest absorption and optimal stability. Here, the role of the halogen mixtures is demonstrated in tuning the contrasting trends of optical absorption and stability. Similarly, binary organic cation mixtures are found to significantly influence the degradation, while they have a lesser, but still visible effect on the opto-electronic properties. The combined framework of high-throughput calculations and ML techniques such as the linear regression methods, random forests and artificial neural networks offers the necessary grounds for an efficient exploration of multi-dimensional compositional spaces.
Phosphorene is a graphene-like material with an intermediate band gap, in contrast to zero-gap graphene and large-gap dichalcogenides or hexagonal boron nitride (hBN), which makes it more suitable for nanoelectronic devices. However, inducing band-gap modulation in freestanding phosphorene nanoribbons (PNRs) is problematic, as high in-plane electric fields are necessary to close the gap. We perform here a detailed investigation concerning the substrate influence on the electric-field control exerted by an external gate, using the density functional theory–non-equilibrium Green’s functions (DFT-NEGF) framework. It is established that the interaction with a hexagonal boron nitride supporting layer significantly enhances the gap modulation. Furthermore, we address the issue of contacting the PNRs, by using conducting graphene nanoribbons embedded in the support hBN layer. Within this setup, a measurable spin polarization is achieved owing to the anti-ferromagnetic coupling between the edges of the graphene nanoribbons.
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