Searcharxiv⌕ Search

arXiv · 2609.31258

Multiphysics Simulation Framework for Continuous Ion Transport and Energetics in Atmospheric Pressure Interfaces

Abstract

Optimizing atmospheric pressure interfaces requires balancing efficient ion transport with unwanted collision activation. The complex environment at the inlet region, including rapid pressure drops, RF/DC electric fields, ion-gas collisions, and space charge effects, makes simulation of this system challenging. In this work, we introduce MuCITE (Multiphysics simulation for Continuous Ion Transport and Energetics), which combines continuous ion injection, precomputed electric and gas flow fields, and particle-in-cell space charge updates. Furthermore, by employing improved impulsive collision theory, it can not only predict transport but also analyze the loss position of ions and the evolution of their kinetic energy, internal energy, and dissociation probability during transport. Transport predictions with ion currents and activation trends of in-source fragmentation were compared with experimental measurements using a custom-built source and a Q-Exactive HF source. The results show that the simulation predictions agree well with the experimental results and can serve as a reference before experiments. MuCITE allows users to derive ion transport, output beam characteristics, and ion energy through the configurable interface geometry and operating conditions. It supports simulation-guided design and tunable parameters to achieve predicted efficient ion transport or in source activation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yihui Yan, Jozef Lengyel, Frederic Rosu, Valerie Gabelica. 2026-09-25. Multiphysics Simulation Framework for Continuous Ion Transport and Energetics in Atmospheric Pressure Interfaces. https://arxiv.org/abs/2609.31258

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

DeepHartree: A Poisson-Coupled Neural Field for One-Shot Density Functional Theory

Density functional theory (DFT) with linear combinations of atomic orbitals (LCAO) is costly because of Coulomb-matrix construction and self-consistent-field (SCF) iterations. We introduce DeepHartree, a Poisson-coupled neural field that connects continuous real-space prediction to LCAO electronic structure. An E(3)-equivariant network predicts the Hartree potential, and the Poisson equation yields the electron density. Atom-centred Gaussian fields resolve near-nuclear structure, and a molecular correction enforces the electron count. Analytical Gaussian integrals and numerical quadrature assemble the Kohn-Sham matrix; one diagonalization yields orbital estimates and a density matrix for energy evaluation and SCF initialization. Mean molecular weighted normalized mean absolute density errors (wNMAEs) are 0.110% for QM9 and 0.199% for VQM24 on PySCF level-0 grids, rising to 1.366% and 2.218% on level-3 grids. Models trained on def2-SVP densities transfer to def2-TZVP and def2-QZVP without retraining, retaining total-energy accuracy and occupied-subspace agreement. A Hybrid method combines finite differences, first-order automatic differentiation, analytical integrals, and multilevel quadrature for near-linear density-matrix construction in alkanes and PEG chains. Excluding grid construction, the density-matrix build for a 1004-atom PEG chain is 8.75-fold faster than full automatic differentiation and 7.34-fold faster than converged GPU4PySCF SCF. Without fine-tuning, the QM9 model achieves a total-energy MAE of 9.437 meV per atom on 1,000 larger OE62 molecules. DeepHartree initialization reduces mean QM9 SCF iterations by 15.3-15.6% across the three basis sets and total SCF time by 12.9% for a 620-atom organic cage, including initialization. These results support continuous electrostatic fields as an accurate, scalable interface between machine learning and LCAO DFT.

physics.chem-ph↗

Learning Electron Density with Virtual Atoms for Transferable Atomistic Representations

Electron density is a fundamental quantity of electronic structure, yet most machine learning density models treat it solely as a reconstruction target and are developed separately from machine learning interatomic potentials. Here, we introduce a virtual-atom formulation within the DPA3 framework, in which density query points are represented as virtual nodes and processed by the same parameterized encoder used for real atoms. An asymmetric neighbor construction allows virtual atoms to receive information from nearby real atoms without altering the real-atom interaction graph. The resulting framework supports density, energy, and force prediction through task-specific readout heads. The proposed model achieves state-of-the-art density reconstruction accuracy on the Materials Project benchmark, matches the best reported performance on nickel-manganese-cobalt battery cathode materials, and is competitive with dedicated density models on QM9. When density labels are used jointly with energy and force supervision, the model improves in-domain accuracy, makes local environment information more accessible under linear probing, and reduces zero-shot force errors on larger QM40 molecules. Density-only checkpoints also provide useful initializations for downstream energy-force learning under the fixed fine-tuning protocol. Together, these results support electron density as a source of electronic-structure supervision for interatomic-potential representations.

physics.chem-ph↗

Assessing the Performance of CC2 for Excited-State Dynamics: A Benchmark Study with Pyrazine

During the ultrafast electronic relaxation of pyrazine, the bright state undergoes rapid decay accompanied by coupled electronic and nuclear motion. We investigate the vibrational modes associated with this process using trajectory surface hopping simulations. To enable accurate \emph{ab initio} excited-state dynamics, we apply CC2 analytical gradients and nonadiabatic coupling vectors within the Q-Chem package. Using both a reduced vibronic coupling model and full-dimensional simulations, we demonstrate that CC2 predicts that the dark $A_\text{1u}$ state actively participates in the initial relaxation and the subsequent dynamics. The full-dimensional simulations yield a $B_\text{2u}$ decay time of 25--26 fs, in good agreement with the experimental measurement of $22\pm3$ fs. Furthermore, we identify the $Q_\text{8a}$ and $Q_\text{9a}$ modes as dominant tuning coordinates whose motions are phase-locked to the population oscillations between the $A_{1u}$ and $B_{3u}$ states.

physics.chem-ph↗