Neuromorphic learning with Mott insulator NiO.

Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. Here, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia, habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability-plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.

Emergence of Ferromagnetism Due to Spontaneous Symmetry Breaking in a Twisted Bilayer Graphene Nanoflex.

Twisted bilayer graphene exhibits many intriguing behaviors ranging from superconductivity to the anomalous Hall effect to ferromagnetism at a magic angle of ∼1°. Here, using a first-principles approach, we reveal ferromagnetism in a twisted bilayer graphene nanoflex. Our results demonstrate that when the energy gap of a twisted nanoflex approaches zero, electronic instability occurs and a ferromagnetic gap state emerges spontaneously to lower the energy. Unlike the observed ferromagnetism at a magic angle in the graphene bilayer, we notice the ferromagnetic phase appearing aperiodically between 0 and 30° in the twisted nanoflex. The origin of electronic instability at various twist angles is ascribed to the several higher-symmetry phases that are broken to lower the energy resulting from an aperiodic modulation of the interlayer interaction in the nanoflex. Besides unraveling a spin-pairing mechanism for the reappearance of the nonmagnetic phase, we have found orbitals at the boundary of nanoflex contributing to ferromagnetism.

How Correlated is the FeSe/SrTiO_{3} System?

Recent observation of ∼10 times higher critical temperature in a FeSe monolayer compared with its bulk phase has drawn a great deal of attention because the electronic structure in the monolayer phase appears to be different than bulk FeSe. Using a combination of density functional theory and dynamical mean field theory, we find electronic correlations have important effects on the predicted atomic-scale geometry and the electronic structure of the monolayer FeSe on SrTiO_{3}. The electronic correlations are dominantly controlled by the Se-Fe-Se angle either in the bulk phase or the monolayer phase. But the angle sensitivity increases and the orbital differentiation decreases in the monolayer phase compared to the bulk phase. The correlations are more dependent on Hund's J than Hubbard U. The observed orbital selective incoherence to coherence crossover with temperature confirms the Hund's metallic nature of the monolayer FeSe. We also find electron doping by oxygen vacancies in SrTiO_{3} increases the correlation strength, especially in the d_{xy} orbital by reducing the Se-Fe-Se angle.

DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides.

With the technological advancement in recent years and the widespread use of magnetism in every sector of the current technology, a search for a low-cost magnetic material has been more important than ever. The discovery of magnetism in alternate materials such as metal chalcogenides with abundant atomic constituents would be a milestone in such a scenario. However, considering the multitude of possible chalcogenide configurations, predictive computational modeling or experimental synthesis is an open challenge. Here, we recourse to a stacked generalization machine learning model to predict magnetic moment (µB) in hexagonal Fe-based bimetallic chalcogenides, FexAyB; A represents Ni, Co, Cr, or Mn, and B represents S, Se, or Te, and x and y represent the concentration of respective atoms. The stacked generalization model is trained on the dataset obtained using first-principles density functional theory. The model achieves MSE, MAE, and R2 values of 1.655 (µB)2, 0.546 (µB), and 0.922 respectively on an independent test set, indicating that our model predicts the compositional dependent magnetism in bimetallic chalcogenides with a high degree of accuracy. A generalized algorithm is also developed to test the universality of our proposed model for any concentration of Ni, Co, Cr, or Mn up to 62.5% in bimetallic chalcogenides.

What determines the sign reversal of magnetoresistance in a molecular tunnel junction?

The observations of both positive and negative signs in tunneling magnetoresistance (TMR) for the same organic spin-valve structure have baffled researchers working in organic spintronics. In this article, we provide an answer to this puzzle by exploring the role of metal-molecule interface on TMR in a single molecular spin-valve junction. A planar organic molecule sandwiched between two nickel electrodes is used to build a prototypical spin-valve junction. A parameter-free, single-particle Green's function approach in conjunction with a posteriori, spin-unrestricted density functional theory involving a hybrid orbital-dependent functional is used to calculate the spin-polarized current. The effect of external bias is explicitly included to investigate the spin-valve behavior. Our calculations show that only a small change in the interfacial distance at the metal-molecule junction can alter the sign of the TMR from a positive to a negative value. By changing the interfacial distance by 3%, the number of participating eigenchannels as well as their orbital characteristics changes for the antiparallel configuration, leading to the sign reversal in TMR.