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Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near ab initio accuracy at significantly reduced computational cost, but their broader adoption is often limited by fragmented tooling, limited scalability,…

Enhancing sampling and analyzing simulations are central issues in molecular simulation. Recently, we introduced PLUMED, an open-source plug-in that provides some of the most popular molecular dynamics (MD) codes with implementations of a…

Computational Physics · Physics 2014-10-07 Gareth A. Tribello , Massimiliano Bonomi , Davide Branduardi , Carlo Camilloni , Giovanni Bussi

Molecular simulation is a scientific tool dealing with challenges in material science and biology. This is reflected in a permanent development and enhancement of algorithms within scientific simulation packages. Here, we present…

New hybrid Molecular Dynamics-Monte Carlo methods are proposed to increase the efficiency of constant-pressure simulations. Two variations of the isobaric Molecular Dynamics component of the algorithms are considered. In the first, we use…

Soft Condensed Matter · Physics 2009-11-07 Roland Faller , Juan J. de Pablo

We present MIST, the Molecular Integration Simulation Toolkit, a lightweight and efficient software library written in C++ which provides an abstract in- terface to common molecular dynamics codes, enabling rapid and portable development of…

A parallelized quantum dynamics package using the Smolyak algorithm for general molecular simulation is introduced in this work. The program has no limitation of the Hamiltonian form and provides high flexibility on the simulation setup to…

Computational Physics · Physics 2021-12-06 Ahai Chen , André Nauts , David Lauvergnat

Equilibrium molecular dynamics simulations are used to calculate the thermal conductivity of the one component plasma (OCP) via the Green-Kubo formalism over a broad range of Coulomb coupling strength, $0.1\le\Gamma\le180$. These…

Plasma Physics · Physics 2019-10-30 Brett Scheiner , Scott D. Baalrud

Dopants can tune the performance of MoS2 in various applications, but use of molecular dynamics simulations for doped MoS2 materials discovery is limited by the lack of multi-dopant interatomic potentials. Universal machine learning…

Materials Science · Physics 2026-03-02 Abrar Faiyad , Ashlie Martini

Heterogeneous multiscale methods (HMM) combine molecular accuracy of particle-based simulations with the computational efficiency of continuum descriptions to model flow in soft matter liquids. In these schemes, molecular simulations…

DL_POLY Quantum 2.1 is introduced here as a highly modular, sustainable, and scalable general-purpose molecular dynamics (MD) simulation software for large-scale long-time MD simulations of condensed phase and interfacial systems with the…

Materials Science · Physics 2025-05-08 Nathan London , Dil K. Limbu , Md Omar Faruque , Farnaz A. Shakib , Mohammad R. Momeni

A new methodology is developed to integrate numerically the equations of motion for classical many-body systems in molecular dynamics simulations. Its distinguishable feature is the possibility to preserve, independently on the size of the…

Statistical Mechanics · Physics 2009-10-31 I. P. Omelyan , I. M. Mryglod , R. Folk

Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared to…

In this paper, we suggest a novel sampling method for Monte Carlo molecular simulations. In order to perform efficient sampling of molecular systems, it is advantageous to avoid extremely high energy configurations while also retaining the…

Computational Physics · Physics 2019-07-18 Katsuhiro Endo , Daisuke Yuhara , Kenji Yasuoka

The separation between molecular and mesoscopic length and time scales poses a severe limit to molecular simulations of mesoscale phenomena. We describe a hybrid multiscale computational technique which address this problem by keeping the…

Fluid Dynamics · Physics 2009-11-13 G. De Fabritiis , R. Delgado-Buscalioni , P. V. Coveney

Enhanced sampling algorithms have emerged as powerful methods to extend the utility of molecular dynamics simulations and allow the sampling of larger portions of the configuration space of complex systems in a given amount of simulation…

Statistical Mechanics · Physics 2022-12-19 Jérôme Hénin , Tony Lelièvre , Michael R. Shirts , Omar Valsson , Lucie Delemotte

A new pairwise hybrid machine-learning/molecular mechanics (ML/MM) potential is introduced that is conceived for application to large, heterogeneous condensed-phase systems. The PhysNet ML method describes monomers and short-range dimer…

Chemical Physics · Physics 2026-03-17 Kham Lek Chaton , Eric D. Boittier , Mike Devereux , Markus Meuwly

We investigate the theory of thermodynamic formalism from the perspective of computable analysis, with a special focus on the computability of equilibrium states. Specifically, we develop two complementary general approaches to verify the…

Dynamical Systems · Mathematics 2025-12-18 Ilia Binder , Qiandu He , Zhiqiang Li , Xianghui Shi

We present a new approach for the two-temperature molecular dynamics (MD) model for coupled simulations of electronic and phonon heat conduction in nanoscale systems. The proposed method uses a master equation to perform heat conduction of…

Computational Physics · Physics 2019-09-04 Mohammad W. Ullah , Mauricio Ponga

Computer simulation is an important tool for scientific progress, especially when lab experiments are either extremely costly and difficult or lack the required resolution. However, all of the simulation methods come with limitations. In…

Fluid Dynamics · Physics 2023-08-04 Edward R. Smith , Panagiotis E. Theodorakis

This review describes recent advances by the authors and others on the topic of incorporating experimental data into molecular simulations through maximum entropy methods. Methods which incorporate experimental data improve accuracy in…

Chemical Physics · Physics 2019-05-15 Dilnoza B. Amirkulova , Andrew D. White
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