Experimental verification of X-point potential well formation in unfavorable magnetic field direction
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Here, we compute the effect of rigid rotation on the non-relativistic bound states. The energy levels of the bound states increase with the angular velocity of rotation until at certain value of the angular velocity they are completely pushed out into the continuum which corresponds to dissociation of the bound states. When the angular velocity exceeds the critical value at which the ground state disappears into the continuum, no bound state is possible. This effect should have important consequences for the phenomenology of the quark-gluon plasma. One of the ways to study it experimentally is to observe the electromagnetic radiation emitted by a rotating bound state. We compute the corresponding intensity of electromagnetic radiation and show that it strongly depends on the angular velocity of rotation.
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Bounce-averaged theories provide a framework for simulating relatively slow processes, such as collisional transport and quasilinear diffusion, by averaging these processes over the fast periodic motions of a particle on a closed orbit. This procedure dramatically increases the characteristic time scale and reduces the dimensionality of the modelled system. The natural coordinates for such calculations are the constants of motion (COM) of the fast particle motion, which by definition do not change during an orbit. However, for sufficiently complicated fields – particularly in the presence of local maxima of the electric potential and magnetic field – the COM are not sufficient to specify the particle trajectory. In such cases, multiple domains in COM space must be used to solve the problem, with boundary conditions enforced between the domains to ensure continuity and particle conservation. Previously, these domains have been imposed by hand, or by recognising local maxima in the fields, limiting the flexibility of bounce-averaged simulations. Here, we present a general set of conditions for identifying consistent domains and the boundary condition connections between the domains, allowing the application of bounce-averaged theories in arbitrarily complicated and dynamically evolving electromagnetic field geometries. We also show how the connections between the domains can be represented by a directed graph, which can help to succinctly represent the trajectory bifurcation structure.
When metals in supported catalysts are atomically dispersed, they are usually cationic and bonded chemically to supports. Investigations of noble metals in this class are growing rapidly, leading to discoveries of catalysts with new properties. Characterization of these materials is challenging because the metal atoms reside on surfaces that are typically nonuniform in composition and structure. We posit that understanding of structures and catalytic properties of these materials is emerging most strongly from investigations of structurally uniform catalysts (metal atoms dispersed on crystalline supports) which can be characterized incisively with atomic-resolution electron microscopy, X-ray absorption spectroscopy, and infrared spectroscopy, bolstered by density functional theory. We assess the literature of such catalysts supported on zeotype materials, metal–organic frameworks, and covalent organic frameworks. Finally, assessing characterization, reactivity, and catalytic performance of catalysts for oxidation, hydrogenation, the water–gas shift reaction, and others, we consider metal–support interactions and ligand effects for various metal–support combinations, evaluating the degree of structural uniformity of exemplary catalysts and summarizing structure–reactivity and structure–catalytic property relationships.
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Kohn–Sham regularizer (KSR) is a differentiable machine learning approach to finding the exchange-correlation functional in Kohn–Sham density functional theory that works for strongly correlated systems. Here we test KSR for a weak correlation. We propose spin-adapted KSR (sKSR) with trainable local, semilocal, and nonlocal approximations found by minimizing density and total energy loss. We assess the atoms-to-molecules generalizability by training on one-dimensional (1D) H, He, Li, Be, and Be 2+ and testing on 1D hydrogen chains, LiH, BeH 2 , and helium hydride complexes. The generalization error from our semilocal approximation is comparable to other differentiable approaches, but our nonlocal functional outperforms any existing machine learning functionals, predicting ground-state energies of test systems with a mean absolute error of 2.7 mH.