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Surface hopping is a widely used method for simulating nonadiabatic dynamics, in which nuclear motion follows classical trajectories and electronic transitions occur stochastically. To ensure energy conservation during these transitions, atomic velocities must be adjusted. Traditional velocity rescaling methods either apply a uniform adjustment to atomic velocities, which can lead to size-consistency issues, or rely on nonadiabatic coupling vectors, which are computationally expensive and may not always be available. Here, we introduce two novel velocity rescaling methods that incorporate atomic contributions to electronic transitions, derived from the one-electron transition density matrix or the density difference between states for a given transition. The first method, , redistributes kinetic energy among atoms proportionally to their contributions to the electronic transition. This is achieved through a weighted scaling factor, computed from the population analysis of the one-electron transition density matrix or the density difference of the two states involved in the transition. The second method, , adjusts the velocities only of atoms whose contributions exceed a predefined threshold, preventing unnecessary energy redistribution to atoms with minimal involvement in the excitation. We validate these approaches through excited-state dynamics simulations of fulvene and 1-1,2,3-triazole. Our results show that excitation-weighted velocity rescaling closely reproduces the adjustments based on nonadiabatic coupling vectors for both fulvene and 1-1,2,3-triazole.
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http://dx.doi.org/10.1021/acs.jctc.5c00737 | DOI Listing |
J Chem Theory Comput
September 2025
University of Vienna, Institute of Theoretical Chemistry, Währinger Str. 17, Vienna A-1090, Austria.
Surface hopping is a widely used method for simulating nonadiabatic dynamics, in which nuclear motion follows classical trajectories and electronic transitions occur stochastically. To ensure energy conservation during these transitions, atomic velocities must be adjusted. Traditional velocity rescaling methods either apply a uniform adjustment to atomic velocities, which can lead to size-consistency issues, or rely on nonadiabatic coupling vectors, which are computationally expensive and may not always be available.
View Article and Find Full Text PDFJ Mol Model
July 2025
Mechanical Engineering, Vanderbilt University, Nashville, TN, USA.
Context: In classical molecular dynamics, thermal transport via electrons is typically non-existent. Therefore, thermal property determination in metals or material systems that include metals is inaccessible. We have developed a two-temperature model for use with non-equilibrium molecular dynamics to predict thermal interface resistance across metal-metal and metal-insulator interfaces.
View Article and Find Full Text PDFPaediatr Perinat Epidemiol
August 2025
Community Health Sciences, Alberta Children's Hospital Research Institute, O'Brien Institute of Public Health, Cumming School of Medicine, University of Calgary, Calgary, Canada.
Background: Experts recommend assessing preterm infant growth against fetal growth patterns. However, obtaining accurate estimates of healthy fetal growth from preterm infants is challenging as many had intrauterine faltering growth.
Objectives: To improve preterm infant growth assessments by developing Fenton third-generation sex-specific preterm growth charts based on anthropometric distributions of preterm infants without abnormal fetal growth.
J Chem Theory Comput
March 2025
Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States.
In this work, we present a generalization of the quantum trajectory surface hopping (QTSH) to multiple states and its implementation in the Libra package for nonadiabatic dynamics. In lieu of the ad hoc velocity rescaling used in many trajectory-based surface hopping approaches, QTSH utilizes quantum forces to evolve nuclear degrees of freedom continuously. It also lifts the unphysical constraint of enforcing the total energy conservation at the individual trajectory level and rather conserves the total energy at the trajectory ensemble level.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2024
This study presents an approach for estimating gaps in arterial diameter using flow velocity obtained from spectral Doppler data. We utilize short-time Fourier transform in conjunction with deep learning models designed for spectrograms to estimate arterial diameter trends. We train the Convolutional Recurrent Neural Network with Attention (CRNN-A) and Audio Spectrogram Transformer (AST) to provide scaled trend estimates in increments of 1, 2, or 4 seconds (s).
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