propnet: A Knowledge Graph for Materials Science

Discovering the ideal material for a new application involves determining its numerous properties, such as electronic, mechanical, or thermodynamic, to match those of its desired application. The rise of high-throughput computation has meant that large databases of material properties are now accessible to scientists. However, these databases contain far more information than might appear at first glance, since many relationships exist in the materials science literature to derive, or at least approximate, additional properties. propnet is a new computational framework designed to help scientists to automatically calculate additional information from their datasets. It does this by constructing a network graph of relationships between different materials properties and traversing this graph. Initially, propnet contains a catalog of over 100 property relationships but the hope is for this to expand significantly in the future, and contributions from the community are welcomed

Exchange magnon induced resistance asymmetry in permalloy spin-Hall oscillators

We investigate magnetization dynamics in a spin-Hall oscillator using a direct current measurement as well as conventional microwave spectrum analysis. When the current applies an anti-damping spin-transfer torque, we observe a change in resistance which we ascribe mainly to the excitation of incoherent exchange magnons. A simple model is developed based on the reduction of the effective saturation magnetization, quantitatively explaining the data. The observed phenomena highlight the importance of exchange magnons on the operation of spin-Hall oscillators

COVID-19 therapy target discovery with context-aware literature mining

The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert. Development of systems, capable of automatically processing tens of thousands of scientific publications with the aim to enrich existing empirical evidence with literature-based associations is challenging and relevant. We propose a system for contextualization of empirical expression data by approximating relations between entities, for which representations were learned from one of the largest COVID-19-related literature corpora. In order to exploit a larger scientific context by transfer learning, we propose a novel embedding generation technique that leverages SciBERT language model pretrained on a large multi-domain corpus of scientific publications and fine-tuned for domain adaptation on the CORD-19 dataset. The conducted manual evaluation by the medical expert and the quantitative evaluation based on therapy targets identified in the related work suggest that the proposed method can be successfully employed for COVID-19 therapy target discovery and that it outperforms the baseline FastText method by a large margin.Comment: Accepted to the 23rd International Conference on Discovery Science (DS 2020

Electrical manipulation of ferromagnetic NiFe by antiferromagnetic IrMn

We demonstrate that an antiferromagnet can be employed for a highly efficient electrical manipulation of a ferromagnet. In our study, we use an electrical detection technique of the ferromagnetic resonance driven by an in-plane ac current in a NiFe/IrMn bilayer. At room temperature, we observe antidampinglike spin torque acting on the NiFe ferromagnet, generated by an in-plane current driven through the IrMn antiferromagnet. A large enhancement of the torque, characterized by an effective spin-Hall angle exceeding most heavy transition metals, correlates with the presence of the exchange-bias field at the NiFe/IrMn interface. It highlights that, in addition to the strong spin-orbit coupling, the antiferromagnetic order in IrMn governs the observed phenomenon

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

Room-temperature spin-orbit torque in NiMnSb

Materials that crystallize in diamond-related lattices, with Si and GaAs as their prime examples, are at the foundation of modern electronics. Simultaneously, inversion asymmetries in their crystal structure and relativistic spin–orbit coupling led to discoveries of non-equilibrium spin-polarization phenomena that are now extensively explored as an electrical means for manipulating magnetic moments in a variety of spintronic structures. Current research of these relativistic spin–orbit torques focuses primarily on magnetic transition-metal multilayers. The low-temperature diluted magnetic semiconductor (Ga, Mn)As, in which spin–orbit torques were initially discovered, has so far remained the only example showing the phenomenon among bulk non-centrosymmetric ferromagnets. Here we present a general framework, based on the complete set of crystallographic point groups, for identifying the potential presence and symmetry of spin–orbit torques in non-centrosymmetric crystals. Among the candidate room-temperature ferromagnets we chose to use NiMnSb, which is a member of the broad family of magnetic Heusler compounds. By performing all-electrical ferromagnetic resonance measurements in single-crystal epilayers of NiMnSb we detect room-temperature spin–orbit torques generated by effective fields of the expected symmetry and of a magnitude consistent with our ab initio calculations.University of WürzburgThis is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nphys377

Spin transport and spin torque in antiferromagnetic devices

Ferromagnets are key materials for sensing and memory applications. In contrast, antiferromagnets which represent the more common form of magnetically ordered materials, have found less practical application beyond their use for establishing reference magnetic orientations via exchange bias. This might change in the future due to the recent progress in materials research and discoveries of antiferromagnetic spintronic phenomena suitable for device applications. Experimental demonstration of the electrical switching and detection of the Néel order open a route towards memory devices based on antiferromagnets. Apart from the radiation and magnetic-field hardness, memory cells fabricated from antiferromagnets can be inherently multilevel, which could be used for neuromorphic computing. Switching speeds attainable in antiferromagnets far exceed those of ferromagnetic and semiconductor memory technologies. Here we review the recent progress in electronic spin-transport and spin-torque phenomena in antiferromagnets that are dominantly of the relativistic quantum mechanical origin. We discuss their utility in pure antiferromagnetic or hybrid ferromagnetic/antiferromagnetic memory devices

Combining Radon transform and Electrical Capacitance Tomography for a 2d+12d+1 imaging device

This paper describes a coplanar non invasive non destructive capacitive imaging device. We first introduce a mathematical model for its output, and discuss some of its theoretical capabilities. We show that the data obtained from this device can be interpreted as a weighted Radon transform of the electrical permittivity of the measured object near its surface. Image reconstructions from experimental data provide good surface resolution as well as short depth imaging, making the apparatus a $2d+1$ imager. The quality of the images leads us to expect that excellent results can be delivered by \emph{ad-hoc} optimized inversion formulas. There are also interesting, yet unexplored, theoretical questions on imaging that this sensor will allow to test

Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature

Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one major problem is the difficulty of identifying which materials from a synthesis paragraph are precursors or are target materials. In this study, we developed a two-step chemical named entity recognition model to identify precursors and targets, based on information from the context around material entities. Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis. To quantify precursor similarity, we built a substitution model to calculate the viability of substituting one precursor with another while retaining the target. From a hierarchical clustering of the precursors, we demonstrate that the "chemical similarity"of precursors can be extracted from text data. Quantifying the similarity of precursors helps provide a foundation for suggesting candidate reactants in a predictive synthesis model