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Ab initio molecular dynamics (AIMD) is a powerful tool to predict properties of molecular and condensed matter systems. The quality of this procedure is based on accurate electronic structure calculations. The development of quantum…

We present in detail the recently derived ab-initio molecular dynamics (AIMD) formalism [Phys. Rev. Lett. 101 096403 (2008)], which due to its numerical properties, is ideal for simulating the dynamics of systems containing thousands of…

The high cost associated with the evaluation of Hartree-Fock exchange (HFX) makes hybrid functionals computationally challenging for large systems. In this work, we present an efficient way to accelerate HFX calculations with numerical…

Computational Physics · Physics 2020-09-09 Xinming Qin , Jie Liu , Wei Hu , Jinlong Yang

For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD applications are limited by computational cost to systems with…

Computational Physics · Physics 2020-09-15 Weile Jia , Han Wang , Mohan Chen , Denghui Lu , Lin Lin , Roberto Car , Weinan E , Linfeng Zhang

We present a new method which combines Car-Parrinello and Born-Oppenheimer molecular dynamics in order to accelerate density functional theory based ab-initio simulations. Depending on the system a gain in efficiency of one to two orders of…

Materials Science · Physics 2007-05-23 Thomas D. Kühne , Matthias Krack , Fawzi R. Mohamed , Michele Parrinello

The accurate simulation of complex biochemical phenomena has historically been hampered by the computational requirements of high-fidelity molecular-modeling techniques. Quantum mechanical methods, such as ab initio wave-function (WF)…

We propose an ab-initio molecular dynamics method, capable to reduce dramatically the autocorrelation time required for the simulation of classical and quantum particles at finite temperature. The method is based on an efficient…

Strongly Correlated Electrons · Physics 2017-01-11 Sandro Sorella , Guglielmo Mazzola

We extend the ab initio molecular dynamics (AIMD) method based on density functional theory to the nonequilibrium situation where an electronic current is present in the electronic system. The dynamics is treated using the semi-classical…

Mesoscale and Nanoscale Physics · Physics 2020-05-27 Jing-Tao Lu , Susanne Leitherer , Nick R. Papior , Mads Brandbyge

We present a hybrid continuum-atomistic scheme which combines molecular dynamics (MD) simulations with on-the-fly machine learning techniques for the accurate and efficient prediction of multiscale fluidic systems. By using a Gaussian…

Fluid Dynamics · Physics 2016-03-16 David Stephenson , James R Kermode , Duncan A Lockerby

Molecular simulations of the forced unfolding and refolding of biomolecules or molecular complexes allow to gain important kinetic, structural and thermodynamic information about the folding process and the underlying energy landscape. In…

Soft Condensed Matter · Physics 2021-05-26 Marco Oestereich , Jürgen Gauss , Gregor Diezemann

All-atom molecular dynamics (MD) computer simulations are a valuable tool for characterizing the conformational ensembles of intrinsically disordered proteins (IDPs). IDP conformational ensembles are highly heterogeneous and contain…

Chemical Physics · Physics 2025-05-06 Jaya Krishna Koneru , Korey M. Reid , Paul Robustelli

We present an efficient algorithm for computing the exact exchange contributions in the Hartree-Fock and hybrid density functional theory models on the basis of the fast multipole method (FMM). Our algorithm is based on the observation that…

Chemical Physics · Physics 2018-01-30 Hai-Anh Le , Toru Shiozaki

The adaptively compressed exchange (ACE) method provides an efficient way for solving Hartree-Fock-like equations in quantum physics, chemistry, and materials science. The key step of the ACE method is to adaptively compress an operator…

Numerical Analysis · Mathematics 2017-11-22 Lin Lin , Michael Lindsey

We demonstrate an efficient and accurate, general-purpose first-principles blueprint for calculating anharmonic vibrational free energy and predicting structural phase transition temperatures of solids. Thermodynamic integration is…

Materials Science · Physics 2024-03-14 Junsoo Park , Zhigang Wu , John W. Lawson

Ultrafast, time-resolved spectroscopies enable the direct observation of non-equilibrium processes in condensed-phase systems and have revealed key insights into energy transport, hydrogen-bond dynamics, and vibrational coupling. While ab…

Chemical Physics · Physics 2025-09-01 Kit Joll , Philipp Schienbein

This study employed an artificial intelligence-enhanced molecular simulation framework to enable efficient Path Integral Molecular Dynamics (PIMD) simulations. Owing to its modular architecture and high-throughput capabilities, the…

Chemical Physics · Physics 2025-04-01 Cheng Fan , Maodong Li , Sihao Yuan , Zhaoxin Xie , Dechin Chen , Yi Isaac Yang , Yi Qin Gao

We present an accelerated ``on-the-fly'' coupled-cluster path-integral molecular dynamics (PIMD) method for finite-temperature simulations in which electron correlation and nuclear quantum effects are treated simultaneously. The approach is…

Chemical Physics · Physics 2026-05-20 Thomas Spura , Hossam Elgabarty , Thomas D. Kühne

We introduce a practical hybrid approach that combines orbital-free density functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles molecular dynamics simulations. Equilibrated ionic configurations are generated using…

We present a multiscale simulation approach for hydroxide transport in aqueous solutions of potassium hydroxide, combining ab initio molecular dynamics (AIMD) simulations with force field ensemble averaging and lattice Monte Carlo…

Computational Physics · Physics 2025-04-09 Jonas Hänseroth , Daniel Sebastiani , Jakob Scholl , Karl Skadell , Christian Dreßler

We push the boundaries of electronic structure-based \textit{ab-initio} molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise barely reachable with classical force-field methods or novel neural network and machine…