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Quantum computing has the potential to reduce the computational cost required for quantum dynamics simulations. However, existing quantum algorithms for coupled electron-nuclear dynamics simulation either require fault-tolerant devices, or…

Quantum Physics · Physics 2026-03-03 Jong-Kwon Ha , Ryan J. MacDonell

A partial differential eigenvalue equation for the density displacement fields associated with electronic excitations is derived in the framework of density functional theory. Our quantum fluid-dynamical approach is based on a variational…

Atomic and Molecular Clusters · Physics 2009-11-07 S. Kümmel , M. Brack

We present a new approach to calculate real-time quantum dynamics in complex systems. The formalism is based on the partitioning of a system's environment into "core" and "reservoir" modes, with the former to be treated quantum mechanically…

Statistical Mechanics · Physics 2015-05-30 Timothy C. Berkelbach , David R. Reichman , Thomas E. Markland

A parameterization strategy for molecular models on the basis of force fields is proposed, which allows a rapid development of models for small molecules by using results from quantum mechanical (QM) ab initio calculations and thermodynamic…

Chemical Physics · Physics 2009-04-22 Bernhard Eckl , Jadran Vrabec , Hans Hasse

In this paper, we present a generic approach of a dynamical data-driven model order reduction technique for three-dimensional fluid-structure interaction problems. A low-order continuous linear differential system is identified from…

Computational Engineering, Finance, and Science · Computer Science 2023-01-25 Claire Dupont , Florian De Vuyst , Anne-Virginie Salsac

We present a method for total energy minimizations and molecular dynamics simulations based either on tight-binding or on Kohn-Sham hamiltonians. The method leads to an algorithm whose computational cost scales linearly with the system…

Condensed Matter · Physics 2009-10-22 Francesco Mauri , Giulia Galli

Computationally cheap yet accurate dynamical models are a key requirement for real-time capable nonlinear optimization and model-based control. When given a computationally expensive high-order prediction model, a reduction to a lower-order…

Systems and Control · Electrical Eng. & Systems 2026-02-20 Jan C. Schulze , Alexander Mitsos

Vibrational spectroscopy is key for probing the interplay between the structure and dynamics of aqueous systems. In order to map different regions of experimental spectra to the microscopic structure of a system, it is important to combine…

Chemical Physics · Physics 2021-08-09 Sam Shepherd , Jinggang Lan , David M. Wilkins , Venkat Kapil

Quantum fluid (or hydrodynamic) models provide an attractive alternative for the modeling and simulation of the electron dynamics in nano-scale objects. Compared to more standard approaches, such as density functional theory or phase-space…

Quantum Physics · Physics 2021-10-12 Giovanni Manfredi , Paul-Antoine Hervieux , Jérôme Hurst

Optimally-shaped electromagnetic fields have the capacity to coherently control the dynamics of quantum systems and thus offer a promising means for controlling molecular transformations relevant to chemical, biological, and materials…

Quantum Physics · Physics 2021-06-08 Alicia B. Magann , Matthew D. Grace , Herschel A. Rabitz , Mohan Sarovar

Complex systems are embedded in our everyday experience. Stochastic modelling enables us to understand and predict the behaviour of such systems, cementing its utility across the quantitative sciences. Accurate models of highly…

High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on…

Numerical Analysis · Mathematics 2023-07-10 Jan S. Hesthaven , Cecilia Pagliantini , Nicolò Ripamonti

High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerators. However, the computational burden of these simulations often limits their use in practice for design optimization and…

Accelerator Physics · Physics 2020-04-15 Auralee Edelen , Nicole Neveu , Yannick Huber , Mattias Frey , Christopher Mayes , Andreas Adelmann

The algebraic reformulation of molecular Quantum Electrodynamics (mQED) at finite temperatures is applied to Nuclear Magnetic Resonance (NMR) in order to provide a foundation for the reconstruction of much more detailed molecular…

Quantum Physics · Physics 2020-09-25 Kolja Them

Quantum molecular dynamics is applied to study the ground state properties of nuclear matter at subsaturation densities. Clustering effects are observed as to soften the equation of state at these densities. The structure of nuclear matter…

We present a novel scheme for modelling quantum plasmas in the warm dense matter (WDM) regime via a hybrid smoothed particle hydrodynamic - molecular dynamic treatment, here referred to as 'Bohm SPH'. This treatment is founded upon Bohm's…

It is known that quantum computers, if available, would allow an exponential decrease in the computational cost of quantum simulations. We extend this result to show that the computation of molecular properties (energy derivatives) could…

Quantum Physics · Physics 2011-03-23 Ivan Kassal , Alán Aspuru-Guzik

In many areas of engineering, nonlinear numerical analysis is playing an increasingly important role in supporting the design and monitoring of structures. Whilst increasing computer resources have made such formerly prohibitive analyses…

Numerical Analysis · Mathematics 2020-07-02 Thomas Simpson , Nikolaos Dervilis , Eleni Chatzi

The implementation of the orbital minimization method (OMM) for solving the self-consistent Kohn-Sham (KS) problem for electronic structure calculations in a basis of non-orthogonal numerical atomic orbitals of finite-range is reported. We…

Computational Physics · Physics 2014-02-06 Fabiano Corsetti

Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. Here, we explore the use of Neural Ordinary Differential Equations, a recently introduced family of…

Machine Learning · Computer Science 2021-04-30 Sourav Dutta , Peter Rivera-Casillas , Matthew W. Farthing