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Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical…

Obtaining the atomistic structure and dynamics of disordered condensed phase systems from first principles remains one of the forefront challenges of chemical theory. Here we exploit recent advances in periodic electronic structure to show…

Simulating water from first principles remains a significant computational challenge due to the slow dynamics of the underlying system. Although machine-learned interatomic potentials (MLPs) can accelerate these simulations, they often fail…

Chemical Physics · Physics 2026-01-30 Tobias Hilpert , Georg Kresse

In this paper we compare different force fields that are widely used (Gromacs, Charmm-22/x-Plor, Charmm-27, Amber-1999, OPLS-AA) in biophysical simulations containing aqueous NaCl. We show that the uncertainties of the microscopic…

Chemical Physics · Physics 2007-05-23 Michael Patra , Mikko Karttunen

This article presents a new force model for performing quantitative simulations of dense granular materials. Interactions between multiple contacts (MC) on the same grain are explicitly taken into account. Our readily applicable method…

Soft Condensed Matter · Physics 2018-10-22 Nicolas Brodu , Joshua A. Dijksman , Robert P. Behringer

We construct a new phase-field model for the solvation of charged molecules with a variational implicit solvent. Our phase-field free-energy functional includes the surface energy, solute-solvent van der Waals dispersion energy, and…

Numerical Analysis · Mathematics 2017-12-07 Yanxiang Zhao , Yanping Ma , Hui Sun , Bo Li , Qiang Du

The work presents the detailed analysis of the water dimer properties. Their parameters are investigated on the basis of a multipole interaction potential extended up to the quadrupole--quadrupole and dipole--octupole terms. All main…

Chemical Physics · Physics 2013-05-02 P. V. Makhlaichuk , M. P. Malomuzh , I. V. Zhyganiuk

Scanning probe microscopy (SPM) has been extensively applied to probe interfacial water in many interdisciplinary fields but the disturbance of the probes on the hydrogen-bonding structure of water has remained an intractable problem. Here…

Cells and other soft particles are often forced to flow in confined geometries in both laboratory and natural environments, where the elastic deformation induces an additional drag and pressure drop across the particle. In contrast with…

Fluid Dynamics · Physics 2025-07-09 Charles Paul Moore , Hiba Belkadi , Brouna Safi , Gabriel Amselem , Charles N. Baroud

The short version of new quantum quantitative Hierarchic theory, general for solids and liquids is presented. Condensed matter is considered as a system of 3D standing waves of different nature: thermal de Broglie waves (waves B), IR…

General Physics · Physics 2009-11-07 Alex Kaivarainen

A microscopic model able to describe simultaneously the dynamic viscosity and the self-diffusion coefficient of fluids is presented. This model is shown to emerge from the introduction of fractional calculus in a usual model of condensed…

Statistical Mechanics · Physics 2021-11-24 F. Aitken , F. Volino

The present work demonstrates a robust protocol for probing localized electronic structure in condensed-phase systems, operating in terms of a recently proposed theory for decomposing the results of Kohn-Sham density functional theory in a…

Chemical Physics · Physics 2021-06-29 Janus J. Eriksen

We present a general scheme to approach the space - time evolution of deformations, currents, and the electric field in charge density waves related to appearance of intrinsic topological defects: dislocations, their loops or pairs, and…

Strongly Correlated Electrons · Physics 2021-03-02 Serguei Brazovskii , Natasha Kirova

In molecular simulations, machine-learning force fields can achieve ab initio accuracy at a lower cost but remain limited in the explicit modeling of electrons. In this work, we develop an electron-aware machine-learning force field, in…

Chemical Physics · Physics 2025-12-01 Ruiqi Gao , Pinchen Xie , Roberto Car

Modelling micro- and meso-scopic scale thermodynamic and transport properties of soft condensed matter hinges upon its representation. This is especially relevant for polar solvents such as water, since these require effective…

Soft Condensed Matter · Physics 2026-04-17 Michael A. Seaton , Benjamin T. Speake , Ilian T. Todorov

In molecular simulations, neural network force fields aim at achieving \emph{ab initio} accuracy with reduced computational cost. This work introduces enhancements to the Deep Potential network architecture, integrating a message-passing…

Computational Physics · Physics 2024-04-09 Ruiqi Gao , Yifan Li , Roberto Car

Molecular fragment or embedding methods are powerful techniques for overcoming scalability limitations in electronic structure theory by dividing large molecular systems into individual units that are small enough to be treated using…

Chemical Physics · Physics 2017-08-14 Jason N. Byrd , Robert W. Molt, , Rodney J. Bartlett , Beverly A. Sanders

Treating water as a linearly responding dielectric continuum on molecular length scales allows very simple estimates of solvation structure and thermodynamics for charged and polar solutes. While this approach can successfully account for…

Statistical Mechanics · Physics 2021-07-07 Stephen J. Cox , Kranthi K. Mandadapu , Phillip L. Geissler

We present an accurate equation of state for water based on a simple microscopic Hamiltonian, with only four parameters that are well-constrained by bulk experimental data. With one additional parameter for the range of interaction, this…

Soft Condensed Matter · Physics 2012-07-27 Ravishankar Sundararaman , Kendra Letchworth-Weaver , T. A. Arias

Electronic structure methods offer in principle accurate predictions of molecular properties, however, their applicability is limited by computational costs. Empirical methods are cheaper, but come with inherent approximations and are…

Chemical Physics · Physics 2023-11-16 Moritz Thürlemann , Sereina Riniker