A Majorana zero bound mode exists in the vortex core of a chiral p + ip superconductor or superfluid, which can be driven from an s-wave pairing state by two-dimensional spinorbit coupling. We ...propose here a novel scheme based on realistic cold atom platforms to generate two-dimensional spin-orbit interactions in a blue-detuned square optical lattice, and predict both the quantum anomalous Hall effect and chiral topological superfluid phase in the experimentally accessible parameter regimes. This work may open a new direction with experimental feasibility to observe non-Abelian topological orders in cold atom systems.
Topological semimetals host electronic structures with several band-contact points or lines and are generally expected to exhibit strong topological responses. Up to now, most work has been limited ...to non-magnetic materials and the interplay between topology and magnetism in this class of quantum materials has been largely unexplored. Here we utilize theoretical calculations, magnetotransport and angle-resolved photoemission spectroscopy to propose Fe
GeTe
, a van der Waals material, as a candidate ferromagnetic (FM) nodal line semimetal. We find that the spin degree of freedom is fully quenched by the large FM polarization, but the line degeneracy is protected by crystalline symmetries that connect two orbitals in adjacent layers. This orbital-driven nodal line is tunable by spin orientation due to spin-orbit coupling and produces a large Berry curvature, which leads to a large anomalous Hall current, angle and factor. These results demonstrate that FM topological semimetals hold significant potential for spin- and orbital-dependent electronic functionalities.
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate ...predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.
SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It ...contains basic building blocks of atomistic neural networks, manages their training, and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. For now, SchNetPack includes implementations of (weighted) atom-centered symmetry functions and the deep tensor neural network SchNet, as well as ready-to-use scripts that allow one to train these models on molecule and material datasets. Based on the PyTorch deep learning framework, SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations, as well as parallelize the model across multiple GPUs. Finally, SchNetPack provides an interface to the Atomic Simulation Environment in order to make trained models easily accessible to researchers that are not yet familiar with neural networks.
The COVID-19 pandemic does not fit into prevailing Post-traumatic Stress Disorder (PTSD) models, or diagnostic criteria, yet emerging research shows traumatic stress symptoms as a result of this ...ongoing global stressor. Current pathogenic event models focus on past, and largely direct, trauma exposure to certain kinds of life-threatening events. Yet, traumatic stress reactions to future, indirect trauma exposure, and non-Criterion A events exist, suggesting COVID-19 is also a traumatic stressor which could lead to PTSD symptomology. To examine this idea, we asked a sample of online participants (N = 1,040), in five western countries, to indicate the COVID-19 events they had been directly exposed to, events they anticipated would happen in the future, and other forms of indirect exposure such as through media coverage. We then asked participants to complete the Posttraumatic Stress Disorder Checklist-5, adapted to measure pre/peri/post-traumatic reactions in relation to COVID-19. We also measured general emotional reactions (e.g., angry, anxious, helpless), well-being, psychosocial functioning, and depression, anxiety, and stress symptoms. We found participants had PTSD-like symptoms for events that had not happened and when participants had been directly (e.g., contact with virus) or indirectly exposed to COVID-19 (e.g., via media). Moreover, 13.2% of our sample were likely PTSD-positive, despite types of COVID-19 "exposure" (e.g., lockdown) not fitting DSM-5 criteria. The emotional impact of "worst" experienced/anticipated events best predicted PTSD-like symptoms. Taken together, our findings support emerging research that COVID-19 can be understood as a traumatic stressor event capable of eliciting PTSD-like responses and exacerbating other related mental health problems (e.g., anxiety, depression, psychosocial functioning, etc.). Our findings add to existing literature supporting a pathogenic event memory model of traumatic stress.
Thanh et al examine the mechanisms of nucleation and growth of nanoparticles in solution. Topics addressed include classical nucleation and growth, theories of nucleation and growth, metal oxides, ...and quantum dots.
High-throughput density functional calculations of solids are highly time-consuming. As an alternative, we propose a machine learning approach for the fast prediction of solid-state properties. To ...achieve this, local spin-density approximation calculations are used as a training set. We focus on predicting the value of the density of electronic states at the Fermi energy. We find that conventional representations of the input data, such as the Coulomb matrix, are not suitable for the training of learning machines in the case of periodic solids. We propose a novel crystal structure representation for which learning and competitive prediction accuracies become possible within an unrestricted class of spd systems of arbitrary unit-cell size.
We theoretically investigate the thermoelectric transport through a circuit implementation of the three-channel charge Kondo model quantum simulator Z. Iftikhar et al., Science 360, 1315 (2018). The ...universal temperature scaling law of the Seebeck coefficient is computed perturbatively approaching the non-Fermi liquid strong coupling fixed point using the Abelian bosonization technique. The predicted T1/3 log T scaling behavior of the thermoelectric power sheds light on the properties of Z3 emerging parafermions and gives access to exploring prefractionalized zero modes in the quantum transport experiments. We discuss a generalization of approach for investigating a multichannel Kondo problem with emergent ZN → ZM crossovers between "weak" non-Fermi liquid regimes corresponding to different low-temperature fixed points.
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