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Register a new userDeep neural network for X-ray photoelectron spectroscopy data analysis
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In this work, we characterize the performance of a deep convolutional neural network designed to detect and quantify chemical elements in experimental X-ray photoelectron spectroscopy data. Given the lack of a reliable database in literature, in order to train the neural network we computed a large ($>$100 k) dataset of synthetic spectra, based on randomly generated materials covered with a layer of adventitious carbon. The trained net performs as good as standard methods on a test set of $approx$ 500 well characterized experimental X-ray photoelectron spectra. Fine details about the net layout, the choice of the loss function and the quality assessment strategies are presented and discussed. Given the synthetic nature of the training set, this approach could be applied to the automatization of any photoelectron spectroscopy system, without the need of experimental reference spectra and with a low computational effort.
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A laboratory hard X-ray photoelectron spectroscopy (HXPS) system equipped with a monochromatic Cr K$alpha$ ($h u = 5414.7$ eV) X-ray source was applied to an investigation of the core-level electronic structure of La$_{1-x}$Sr$_x$MnO$_3$. No appreciable high binding-energy shoulder in the O $1s$ HXPS spectra were observed while an enhanced low binding-energy shoulder structure in the Mn $2p_{3/2}$ HXPS spectra were observed, both of which are manifestation of high bulk sensitivity. Such high bulk sensitivity enabled us to track the Mn $2p_{3/2}$ shoulder structure in the full range of $x$, giving us a new insight into the binding-energy shift of the Mn $2p_{3/2}$ core level. Comparisons with the results using the conventional laboratory XPS ($h u = 1486.6$ eV) as well as those using a synchrotron radiation source ($h u = 7939.9$ eV) demonstrate that HXPS is a powerful and convenient tool to analyze the bulk electronic structure of a host of different compounds.
We develop a spacetime neural network method with second order optimization for solving quantum dynamics from the high dimensional Schr{o}dinger equation. In contrast to the standard iterative first order optimization and the time-dependent variational principle, our approach utilizes the implicit mid-point method and generates the solution for all spatial and temporal values simultaneously after optimization. We demonstrate the method in the Schr{o}dinger equation with a self-normalized autoregressive spacetime neural network construction. Future explorations for solving different high dimensional differential equations are discussed.
Photoelectron spectroscopy (PES) and microscopy are highly demanded for exploring morphologically complex solid-gas and solid-liquid interfaces under realistic conditions, but the very small electron mean free path inside the dense media imposes serious experimental challenges. Currently, near ambient pressure PES is conducted using sophisticated and expensive electron energy analyzers coupled with differentially pumped electron lenses. An alternative economical approach proposed in this report uses ultrathin graphene membranes to isolate the ambient sample environment from the PES detection system. We demonstrate that the graphene membrane separating windows are both mechanically robust and sufficiently transparent for electrons in a wide energy range to allow PES of liquid and gaseous water. The reported proof-of-principle experiments also open a principal possibility to probe vacuum-incompatible toxic or reactive samples enclosed inside the hermetic environmental cells.
We report on the crossover from the thermal to athermal regime of an artificial spin ice formed from a square array of magnetic islands whose lateral size, 30~nm~$times$~70~nm, is small enough that they are superparamagnetic at room temperature. We used resonant magnetic soft x-ray photon correlation spectroscopy (XPCS) as a method to observe the time-time correlations of the fluctuating magnetic configurations of spin ice during cooling, which are found to slow abruptly as a freezing temperature $T_0 = 178 pm 5$~K is approached. This slowing is well-described by a Vogel-Fulcher-Tammann law, implying that the frozen state is glassy, with the freezing temperature being commensurate with the strength of magnetostatic interaction energies in the array. The activation temperature, $T_mathrm{A} = 40 pm 10$~K, is much less than that expected from a Stoner-Wohlfarth coherent rotation model. Zero-field-cooled/field-cooled magnetometry reveals a freeing up of fluctuations of states within islands above this temperature, caused by variation in the local anisotropy axes at the oxidised edges. This Vogel-Fulcher-Tammann behavior implies that the system enters a glassy state on freezing, which is unexpected for a system with a well-defined ground state.
We present a simplified web-based application for simulating x-ray and photoelectron spectra of transition metals, built around the notion that web-based applications lower the bar for novice users. The application provides a simple interface to simulate x-ray absorption spectroscopy, resonant inelastic x-ray scattering, and angle-resolved photoemission spectroscopy, incorporating the effects of local electronic interactions, which give rise to multiplets, spin-orbit coupling, crystal field effects, and ligand hybridization/charge transfer. Results can be obtained that highlight the key role of photon polarization.
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