Dynamic hysteresis effects have been long known to occur in the current density-voltage characteristics of perovskite solar cells, with the ionic migration being identified as the primary factor. The hysteretic effects impacted early studies by the uncertainty in the evaluation of the power conversion efficiency, while currently, potential links to degradation mechanisms are the focus. Therefore, understanding ion migration is a central goal, typically addressed by performing a combined large and small signal analysis. The reported large capacitive and inductive effects created controversies with respect to the underlying mechanisms, yielding essentially two classes of models, one based on the large accumulation capacitances and the other based on the ionic modulation of the collected current. We introduce here an equivalent circuit model and interpret these phenomena in terms of recombination current modulation, identifying the distinct contributions from ion current and ionic charge accumulations. These contributions to the recombination current are associated with capacitive and inductive effects, respectively, and we corroborate the numerical simulations with electrochemical impedance spectroscopy measurements. These show the role of the recombination currents of photogenerated carriers in producing both capacitive and inductive effects as the illumination is varied. Moreover, we provide a bridging point between the two classes of models and suggest a framework of investigation of defect states based on the observed inductive behavior, which would further aid the mitigation of the degradation effects.

We propose a workflow which includes the essential step of feature selection in order to optimize combined density functional theory—machine learning schemes (DFT-ML). Here, the energy gaps of hybrid graphene—boron nitride nanoflakes with randomly distributed domains are predicted using artificial neural networks (ANNs). The training data is obtained by associating structural information to the target quantity of interest, i.e. the energy gap, obtained by DFT calculations. The selection of proper feature vectors is important for an accurate and efficient ANN model. However, finding an optimal set of features is generally not trivial. We compare different approaches for selecting the feature vectors, ranging from random selection of the features to guided approaches like removing the features with lowest variance and by using the mutual information regression selection technique. We show that the feature selection procedures provides a significant reduction of the input space dimensionality. In addition, a selection method based on the ranking of the cutting radius is proposed and evaluated. This may not only be important for establishing optimal ANN models, but may offer insights into the minimum information required to map certain targeted properties.

High entropy alloys (HEAs) are still a largely unexplored class of materials with high potential for applications in various fields. Motivated by the huge number of compounds in a given HEA class, we develop machine learning techniques, in particular artificial neural networks, coupled to ab initio calculations, in order to accurately predict some basic HEA properties: equilibrium phase, cohesive energies, density of states at the Fermi level and the stress-strain relation, under conditions of isotropic deformations. Known for its high tensile ductility and fracture toughness, the Co-Cr-Fe-Ni-Al alloy has been considered as a test candidate material, particularly by adjusting the Al content. However, further enhancement of the microstructure, mechanical and thermal properties is possible by modifying also the fractions of the base alloy. Using deep neural networks, we map structural and chemical neighborhood information onto the quantities of interest. This approach offers the possibility for an efficient screening over a huge number of potential candidates, which is essential in the exploration of multi-dimensional compositional spaces.

The feasibility of mixed-cation mixed-halogen perovskites of formula A${}_x$A’${}_{1-x}$PbX${}_y$X’${}_z$X”${}_{3-y-z}$ 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.

We report the facile and low-cost preparation as well as detailed characterization of dense arrays of passivated ferromagnetic nickel (Ni) nanotubes (NTs) vertically-supported onto solid Au-coated Si substrates. The proposed fabrication method relies on electrochemical synthesis within the nanopores of a supported anodic aluminum oxide (AAO) template and allows for fine tuning of the NTs ferromagnetic walls just by changing the cathodic reduction potential during the nanostructures’ electrochemical growth. Subsequently, the experimental platform allowed further passivation of the Ni NTs with the formation of ultra-thin antiferromagnetic layers of nickel oxide (NiO). Using adequately adapted magnetic measurements, we afterwards demonstrated that the thickness of the NT walls and of the thin antiferromagneticNiO layer, strongly influences the magnetic behavior of the dense array of exchange-coupled Ni/NiO NTs. The specific magnetic properties of these hybrid ferromagnetic/antiferromagnetic nanosystems were then correlated with the morpho-structural and geometrical parameters of the NTs, as well as ultimately strengthened by additionally-implemented micromagnetic simulations. The effect of the unidirectional anisotropy strongly amplified by the cylindrical geometry of the ferromagnetic/antiferromagnetic interfaces has been investigated with the magnetic field applied both parallel and perpendicular to the NTs axis.