Publications associées à Expanding density-correlation machine learning representations for anisotropic coarse-grained particles

Publications associées (10)

Momentum tunnelling between nanoscale liquid flows

The world of nanoscales in fluidics is the frontier where the continuum of fluid mechanics meets the atomic, and even quantum, nature of matter. While water dynamics remains largely classical under extreme confinement, several experiments have recently rep ...

2025

Wear across scales

Sacha Zenon Wattel

When two objects are rubbed together, they create friction, heat, and their surfaces might be worn away. However, the fundamental mechanisms behind these processes remain poorly understood. While empirical engineering models exist, they are tailored to spe ...

EPFL2025

Predicting protein interactions using geometric deep learning on protein surfaces

Freyr Sverrisson

In the domain of computational structural biology, predicting protein interactions based on molecular structure remains a pivotal challenge. This thesis delves into this challenge through a series of interconnected studies. The first chapter introduces th ...

EPFL2024

DeePMD-kit v2: A software package for deep potential models

Davide Tisi

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of phy ...

AIP Publishing2023

Recent Advances in First-Principles Based Molecular Dynamics

Ursula Röthlisberger, Justin Villard, François Louis Mouvet, Viacheslav Bolnykh

First-principles molecular dynamics (FPMD) and its quantum mechanical-molecular mechanical (QM/MM) extensions are powerful tools to follow the real-time dynamics of a broad variety of systems in their ground as well as electronically excited states. The co ...

American Chemical Society2022

Electronic-structure methods for materials design

Nicola Marzari

Simulations can be used to accelerate the characterization and discovery of materials. Here we Review how electronic-structure methods such as density functional theory work, what properties they can be used to predict and how they can be used to design ma ...

NATURE RESEARCH2021

CLoNe: automated clustering based on local density neighborhoods for application to biomolecular structural ensembles

Matteo Dal Peraro, Deniz Aydin, Giorgio Elikem Tamo, Martina Audagnotto, Giulia Fonti, Sylvain Träger

Motivation: Proteins are intrinsically dynamic entities. Flexibility sampling methods, such as molecular dynamics or those arising from integrative modeling strategies, are now commonplace and enable the study of molecular conformational landscapes in many ...

Oxford University Press2021

Atomistic modeling of the solid-liquid interface of metals and alloys

Edoardo Baldi

Solidification is a phase transformation of utmost importance in material science, for it largely controls materials' microstructure on which a wide range of mechanical properties depends. Almost every human artifact undergoes a transformation that leads t ...

EPFL2020

Libraries of Extremely Localized Molecular Orbitals. 3. Construction and Preliminary Assessment of the New Databanks

Benjamin André René Meyer

The fast and reliable determination of wave functions and electron densities of macromolecules has been one of the goals of theoretical chemistry for a long time, and in this context, several linear scaling techniques have been successfully devised over th ...

AMER CHEMICAL SOC2018

Negative-pressure-induced enhancement in a freestanding ferroelectric

Nava Setter, Alexander Tagantsev, Marco Cantoni, Silviu Cosmin Sandu, Tomas Sluka, Jin Wang, Alexander Kvasov

Ferroelectrics are widespread in technology(1), being used in electronics and communications(2), medical diagnostics and industrial automation. However, extension of their operational temperature range and useful properties is desired(3-5). Recent developm ...

Nature Publishing Group2015