An improved set of electron-THFA cross sections refined through a neural network-based analysis of swarm data

Abstract

This paper was selected as an Editor's Pick ARTICLES YOU MAY BE INTERESTED INEfficient enumeration of bosonic configurations with applications to the calculation of nonradiative rates

Simulating the Feasibility of Using Liquid Micro-Jets for Determining Electron–Liquid Scattering Cross-Sections

“…We have developed a joint Monte Carlo and machine learning solution to the inverse Milne problem, that extracts electron cross-sections based on electron energy loss spectra from a micro-jet of dense gas. Machine learning was conducted using similar techniques outlined in the literature [ 41 , 42 , 43 , 44 ], while a new Monte Carlo simulation of a liquid micro-jet was developed. As a proof-of-concept, we found that the neural network determined neon’s gas phase elastic momentum transfer cross-sections to within .…”

“…In multiple studies [ 41 , 42 , 43 , 45 , 46 ], machine learning was applied to the inversion of transport coefficients found through swarm experiments. While swarm transport coefficient measurements differ in nature to EELS, they each represent an ill-posed inversion problem associated with high degrees of multiple scattering.…”

“…To obtain a solution to the “inverse EELS problem”, we apply the same general machine learning approach as proposed by Stokes et al [ 41 , 42 , 43 , 44 ], for the analysis of electron swarm transport data, and utilise an artificial neural network of the form: where are affine mappings defined by dense weight matrices and bias vectors , and is a nonlinear activation function [ 91 ] that is applied element-wise. The output vector, , contains each cross-section of interest: all of which are a function of energy, , which becomes an input to the neural network alongside the available EELS data: where are the electron intensities accumulated in each of the considered EELS energy bins.…”

“…The same techniques can be used, in principle, to iteratively improve or develop cross-section sets [ 35 , 36 , 37 , 38 , 39 , 40 ], although the question of degeneracy in the cross-section set remains open, and other means are usually required to minimise this. Recent studies have shown promising applications of neural networks (NN), toward improving cross-section sets through swarm transport data [ 41 , 42 , 43 , 44 , 45 , 46 ]. Utilising a similar NN methodology, along with energy loss spectra from a LµJ experiment, we propose the generation of cross-section sets for electrons within a liquid environment.…”

Electron-impact excitation of the (4d105s)S1/22→

“…To quantitatively undertake Monte Carlo simulations or multiterm Boltzmann equation modeling (see, e.g., Refs. [13][14][15][16]) of a given system, Tanaka et al [17] and Brunger [18] noted that a comprehensive and complete cross-section database is required. Therefore, we surmise that further work on e-Ag collisional behavior is required and this forms one rationale for the present investigation.…”