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Optimal exploitation of supercomputing resources for the evaluation of electrostatic forces remains a challenge in molecular dynamics simulations of very large systems. The most efficient methods are currently based on particle-mesh Ewald…

Computational Physics · Physics 2025-09-23 Federica Troni , Davide Grassano , Jayashree Narayan , Benoît Roux , Sara Bonella

We formulate a model of N_f=4 flavors of relativistic fermion in 2+1d in the presence of a chemical potential mu coupled to two flavor doublets with opposite sign, akin to isopsin chemical potential in QCD. This is argued to be an effective…

High Energy Physics - Lattice · Physics 2013-05-15 Wes Armour , Simon Hands , Costas Strouthos

We present a semi-anisotropic interfacial potential (SAIP) designed to classically describe the interaction between gold and two-dimensional (2D) carbon allotropes such as graphene, fullerenes, or hydrocarbon molecules. The potential is…

Materials Science · Physics 2021-09-30 Wengen Ouyang , Oded Hod , Roberto Guerra

The perfect transmission of charge carriers through potential barriers in graphene (Klein tunneling) is a direct consequence of the Dirac equation that governs the low-energy carrier dynamics. As a result, localized states do not exist in…

Graphene, the 2D form of carbon, has excellent mechanical, electrical and thermal properties and a variety of potential applications including NEMS, protective coatings, transparent electrodes in display devices and biological applications.…

Mesoscale and Nanoscale Physics · Physics 2018-08-20 Narasimha G Boddeti , Rong Long , Martin L Dunn

Most of the performances of electrochemical devices are governed by molecular processes taking place at the solution-electrode interfaces and molecular simulation are the main way to study these processes. Aqueous electrochemical systems…

Chemical Physics · Physics 2019-12-12 Guillaume Jeanmairet , Benjamin Rotenberg , Daniel Borgis , Mathieu Salanne

One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as the density functional theory (DFT)…

Many objects of nanoscopic dimensions involve fluid-tethered chain interfaces. These systems are of interest for basic science and for several applications, in particular for design of nanodevices for specific purposes. We review recent…

Soft Condensed Matter · Physics 2014-03-07 S. Sokołowski , J. Ilnytskyi , O. Pizio

The standard model of classical Density Functional Theory for pair potentials consists of a hard-sphere functional plus a mean-field term accounting for long ranged attraction. However, most implementations using sophisticated Fundamental…

Computational Physics · Physics 2021-01-04 James F. Lutsko , Cédric Schoonen

The interface structure of Fe/MgO(100) magnetic tunnel junctions predicted by density functional theory (DFT) depends significantly on the choice of exchange and correlation functional. Bader analysis reveals that structures obtained by…

Materials Science · Physics 2015-05-13 Xiaobing Feng , O. Bengone , M. Alouani , S. Lebégue , I. Rungger , S. Sanvito

The proper design and synthesis of molecular junctions for the purpose of establishing percolative networks of conductive nanoparticles represent an opportunity to develop more efficient thermally-conductive nanocomposites, with several…

Among its many outstanding properties, graphene supports terahertz surface plasma waves -- sub-wavelength charge density oscillations connected with electromagnetic fields that are tightly localized near the surface[1,2]. When these waves…

Ionic liquids (IL) are promising electrolytes for electrochemical applications due to their remarkable stability and high charge density. Molecular dynamics simulations are essential for better understanding the complex phenomena occurring…

Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive. Learning-based force fields have made significant progress in accelerating ab-initio MD simulation but are not fast enough for…

Machine Learning · Computer Science 2023-08-29 Xiang Fu , Tian Xie , Nathan J. Rebello , Bradley D. Olsen , Tommi Jaakkola

Graphene is one of the most researched two dimensional (2D) material due to its unique combination of mechanical, thermal and electrical properties. Special 2D structure of graphene enables it to exhibit a wide range of peculiar material…

Computational Physics · Physics 2023-06-13 Akash Singh , Yumeng Li

We show that the Gaussian Approximation Potential machine learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total…

Materials Science · Physics 2018-02-07 Daniele Dragoni , Thomas D. Daff , Gabor Csanyi , Nicola Marzari

Controllable synthesis of defect-free graphene is crucial for applications since the properties of graphene are highly sensitive to any deviations from the crystalline lattice. We focus here on the emerging use of liquid Cu catalysts, which…

In this study, the analysis of the mechanical response of realistic fullerene-nanotube nanotruss networks with face-centered cubic geometry is performed by using molecular dynamics with reactive potentials. In particular, the mechanical…

Mesoscale and Nanoscale Physics · Physics 2017-07-04 Andrea Pedrielli , Simone Taioli , Giovanni Garberoglio , Nicola Pugno

In the present work we detail how the many-body potential energy landscape of interatomic potentials for carbon can be explored by utilising the nested sampling algorithm, allowing the calculation of their pressure-temperature phase diagram…

Materials Science · Physics 2022-08-23 George Marchant , Bora Karasulu , Livia B. Partay

We generalize the scalable tight-binding model for graphene, which allows for efficient quantum transport simulations in the Dirac regime, to account for elastic strain. We show that the original scalable model with scaling factor $s$ is…

Mesoscale and Nanoscale Physics · Physics 2026-05-28 Ming-Hao Liu , Christophe De Beule , Alina Mreńca-Kolasińska , Hsin-You Wu , Aitor Garcia-Ruiz , Denis Kochan , Klaus Richter