Related papers: IKEBANA: A Neural-Network approach for the K-shell…

We investigate whether a neural network approach can reproduce and predict the electron-nucleus cross sections in the kinematical domain of present and future accelerator-based neutrino oscillation experiments. For this purpose, we consider…

We calculate the cross section of the electron scattering from a bound nucleon within light-front approximation. The advantage of this approximation is the possibility of systematic account for the off-shell effects which become essential…

Machine-learning models in chemistry - when based on descriptors of atoms embedded within molecules - face essential challenges in transferring the quality of predictions of local electronic structures and their associated properties across…

We demonstrate the possibility of realizing a neural network in a chain of trapped ions with induced long range interactions. Such models permit one to store information distributed over the whole system. The storage capacity of such…

We reduce measurement errors in a quantum computer using machine learning techniques. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data. This flexible approach…

The shape and absolute values of the generalized cross-sections of the two-photon resonant single ionization of the K-shell of ions of the isonuclear sequence of a heavy nickel atom (28Ni - Ni26+ - Ni24+ - Ni18+) have been theoretically…

We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information…

An outstanding challenge in chemical computation is the many-electron problem where computational methodologies scale prohibitively with system size. The energy of any molecule can be expressed as a weighted sum of the energies of…

An extension of the analytical model of Talukder et al (Int. J. Mass Spectrom. 269 (2008) 118) is proposed to estimate electron impact single L- and M-shell ionization cross sections of atoms with incident energy from threshold to…

We use the recently introduced independent-atom-model pixel counting method to calculate proton-impact net ionization cross sections for a large class of biologically relevant systems including pyrimidines, purines, amino acids, and…

The cross section for single K-shell ionization by a high-energy photon is evaluated in the next-to-leading order of the nonrelativistic perturbation theory with respect to the electron-electron interaction. The screening corrections are of…

The K x-ray spectra of different targets (Cu, Zn, and Ge) induced by 3 to 5 MeV/u Si projectile ions have been measured to determine the K-shell ionization cross-section. A significant difference is observed between the measurements and…

Artificial neural networks bridge input data into output results by approximately encoding the function that relates them. This is achieved after training the network with a collection of known inputs and results leading to an adjustment of…

We have investigated the universal scaling behavior of differential cross sections for the single K-shell ionization by electron or positron impact. The study is performed within the framework of non-relativistic perturbation theory, taking…

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…

We provide in this paper a concrete method for training a quantum neural network to maximize the relevant information about a property that is transmitted through the network. This is significant because it gives an operationally well…

We present machine learning models based on kernel-ridge regression for predicting X-ray photoelectron spectra of organic molecules originating from the $K$-shell ionization energies of carbon (C), nitrogen (N), oxygen (O), and fluorine (F)…

We present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model and predict nuclear dipole responses. We train the network on a dataset of experimentally measured…

The universal scaling behavior for the electron-impact excitation cross sections of the $2s$ states of hydrogen- and helium-like multicharged ions is deduced. The study is performed within the framework of non-relativistic perturbation…

We demonstrate a machine learning based approach which can learn the time-dependent electronic excitation dynamics of small molecules subjected to ion irradiation. Ensembles of recurrent neural networks are trained on data generated by…