Antiferromagnetic spintronics

Antiferromagnetic materials are magnetic inside, however, the direction of their ordered microscopic moments alternates between individual atomic sites. The resulting zero net magnetic moment makes magnetism in antiferromagnets invisible on the outside. It also implies that if information was stored in antiferromagnetic moments it would be insensitive to disturbing external magnetic fields, and the antiferromagnetic element would not affect magnetically its neighbors no matter how densely the elements were arranged in a device. The intrinsic high frequencies of antiferromagnetic dynamics represent another property that makes antiferromagnets distinct from ferromagnets. The outstanding question is how to efficiently manipulate and detect the magnetic state of an antiferromagnet. In this article we give an overview of recent works addressing this question. We also review studies looking at merits of antiferromagnetic spintronics from a more general perspective of spin-ransport, magnetization dynamics, and materials research, and give a brief outlook of future research and applications of antiferromagnetic spintronics.Comment: 13 pages, 7 figure

Limits on Fundamental Limits to Computation

An indispensable part of our lives, computing has also become essential to industries and governments. Steady improvements in computer hardware have been supported by periodic doubling of transistor densities in integrated circuits over the last fifty years. Such Moore scaling now requires increasingly heroic efforts, stimulating research in alternative hardware and stirring controversy. To help evaluate emerging technologies and enrich our understanding of integrated-circuit scaling, we review fundamental limits to computation: in manufacturing, energy, physical space, design and verification effort, and algorithms. To outline what is achievable in principle and in practice, we recall how some limits were circumvented, compare loose and tight limits. We also point out that engineering difficulties encountered by emerging technologies may indicate yet-unknown limits.Comment: 15 pages, 4 figures, 1 tabl

Spin-transfer torque induced reversal in magnetic domains

Using the complex stereographic variable representation for the macrospin, from a study of the nonlinear dynamics underlying the generalized Landau-Lifshitz(LL) equation with Gilbert damping, we show that the spin-transfer torque is effectively equivalent to an applied magnetic field. We study the macrospin switching on a Stoner particle due to spin-transfer torque on application of a spin polarized current. We find that the switching due to spin-transfer torque is a more effective alternative to switching by an applied external field in the presence of damping. We demonstrate numerically that a spin-polarized current in the form of a short pulse can be effectively employed to achieve the desired macro-spin switching.Comment: 16 pages, 6 figure

Modulating Dopant-Defect Interactions in Transition Metal Doped Colloidal Strontium Titanate Nanocrystals

Perovskites such as strontium titanate, a wide band gap semiconductor have been widely studied due to the multitude of potential applications in photocatalysis, multiferroics, sensing, and microelectronics. Various novel optical, electrical and magnetic properties can be imparted through the introduction of different transition metal dopant ions. The introduction of these impurities has been shown to impart functionality for various applications. The use of Cr3+has been shown to introduce defect levels into the band structure of SrTiO3and increase visible light utilization for photocatalysis. Transition metal doped highly crystalline colloidal SrTiO3nanocrystals (NC) were synthesized using two modified hydrothermal (HT) synthesis methods and subsequently characterized. Structural characterization techniques such as powder X-ray diffraction (XRD), tunneling electron microscopy (TEM) and high-resolution tunneling electron microscopy (HR-TEM) proved vital in confirming the successful synthesis of highly crystalline sub-10 nm nanocubes. The use of transition metal doped colloidal nanocrystals will look to better understand dopant incorporation and subsequent defect formation and their interactions on the nm scale. The use of dopant specific spectroscopies such as electron paramagnetic resonance (EPR) spectroscopy was crucial in determining both the transition metal dopant oxidation state and dopant position in the colloidal NCs. Through the use of EPR spectroscopy the dopants and defects were unambiguously identified and characterized. Various surface related oxygen defects were identified and correlated to emerge under specific synthetic conditions. Site-selective internal doping of Cr or Mn was achieved through either modified HT methods. The extent of the dopant-defect interactions was studied using a photodoping technique. Under anaerobic conditions and in the presence of a hole quencher, the interactions of the photoinduced electron with SrTiO3were monitored. The photoinduced changes were tracked using electronic absorption spectroscopy and EPR spectroscopy. An in-depth EPR analysis showed the photoinduced electron localizes on titanium atoms and undergoes cross relaxation with neighboring Cr ions

Data driven discovery of materials properties.

The high pace of nowadays industrial evolution is creating an urgent need to design new cost efficient materials that can satisfy both current and future demands. However, with the increase of structural and functional complexity of materials, the ability to rationally design new materials with a precise set of properties has become increasingly challenging. This basic observation has triggered the idea of applying machine learning techniques in the field, which was further encouraged by the launch of the Materials Genome Initiative (MGI) by the US government since 2011. In this work, we present a novel approach to apply machine learning techniques for materials science applications. Guided by knowledge from domain experts, our approach focuses on machine learning to accelerate data-driven discovery of materials properties. Our objectives are two folds: (i) Identify the optimal set of features that best describes a given predicted variable. (ii) Boost prediction accuracy via applying various regression algorithms. Ordinary Least Square, Partial Least Square and Lasso regressions, combined with well adjusted feature selection techniques are applied and tested to predict key properties of semiconductors for two types of applications. First, we propose to build a more robust prediction model for band-gap energy (BG-E) of chalcopyrites, commonly used for solar cells industry. Compared to the results reported in [1-3] , our approach shows that learning and using only a subset of relevant features can improve the prediction accuracy by about 40%. For the second application, we propose to determine the underlying factors responsible for Defect-Induced Magnetism (DIM) in Dilute Magnetic Semiconductors (DMS) through the analysis of a set of 30 features for different DMS systems. We show that 8 of these features are more likely to contribute to this property. Using only these features to predict the total magnetic moment of new candidate DMSs has reduced the mean square error by about 90% compared to the models trained using the whole set of features. Given the scarcity of the available data sets for similar applications, this work aims not only to build robust models but also to establish a collaborative platform for future research

Colloquium: Spintronics in graphene and other two-dimensional materials

After the first unequivocal demonstration of spin transport in graphene (Tombros et al., 2007), surprisingly at room temperature, it was quickly realized that this novel material was relevant for both fundamental spintronics and future applications. Over the decade since, exciting results have made the field of graphene spintronics blossom, and a second generation of studies has extended to new two-dimensional (2D) compounds. This Colloquium reviews recent theoretical and experimental advances on electronic spin transport in graphene and related 2D materials, focusing on emergent phenomena in van der Waals heterostructures and the new perspectives provided by them. These phenomena include proximity-enabled spin-orbit effects, the coupling of electronic spin to light, electrical tunability, and 2D magnetism.Comment: 30 pages, 12 figure