Publications associées à Recent Advances in First-Principles Based Molecular Dynamics

Publications associées (14)

Wear across scales

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

Momentum tunnelling between nanoscale liquid flows

Nikita Kavokine

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

Advances in the modeling and simulation of NTPs using OpenFOAM and OpenModelica

Alessandro Scolaro, Thomas Arthur Louis Joseph Guilbaud, Giovanni Nervi

American Nuclear Society2025

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

Jigyasa Nigam, Kevin Kazuki Huguenin-Dumittan

Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotro ...

2024

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

Hybrid ground-state quantum algorithms based on neural Schrödinger forging

Giuseppe Carleo

Entanglement forging based variational algorithms leverage the bipartition of quantum systems for addressing ground-state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis stat ...

Amer Physical Soc2024

Towards Trustworthy Deep Learning for Image Reconstruction

Alexis Marie Frederic Goujon

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

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

Bacterial Lectin FimH and Its Aggregation Hot-Spots: An Alternative Strategy against Uropathogenic Escherichia coli

Konstantina Georgakopoulou

Type I fimbriae are the main adhesive organelles of uropathogenic Escherichia coli (UPEC), consisting of four different subunits. Their component with the most important role in establishing bacterial infections is the FimH adhesin located at the fimbrial ...

2023

Deep Learning Meets Sparse Regularization

Rahul Parhi

Deep learning (DL) has been wildly successful in practice, and most of the state-of-the-art machine learning methods are based on neural networks (NNs). Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of ...

2023

Realistic Modelling of Dynamics at Nanostructured Interfaces Relevant to Heterogeneous Catalysis

Kevin Rossi

The focus of this short review is directed towards investigations of the dynamics of nanostructured metallic heterogeneous catalysts and the evolution of interfaces during reaction-namely, the metal-gas, metal-liquid, and metal-support interfaces. Indeed, ...

MDPI2022

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale

Christos Kotsalos, Tristan Mathieu Carel, Samuel Marie A Melchior, Baudouin Paul Michel Maria Joseph Del Marmol, Giacomo Castiglioni, Nicola Cantarutti, Felix Schürmann, James Gonzalo King, Pramod Shivaji Kumbhar, Omar Awile, Alessandro Cattabiani

Recent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel imp ...

FRONTIERS MEDIA SA2022

Practical Byzantine-resilient Stochastic Gradient Descent

Sébastien Louis Alexandre Rouault

Algorithms are everywhere. The recipe for the frangipane cake is an algorithm. If all the listed ingredients are available and the cook is sufficiently deft, after a finite number of small, well-defined steps a delicious dessert will exit the oven. Now, wh ...

EPFL2022

Experimental Method to Distinguish between a Solution and a Suspension

Francesco Stellacci, Quy Ong Khac, Paulo Henrique Jacob Silva, Xufeng Xu, Ting Mao

Dispersion of objects in a fluid phase can be classified as solutions (Gibbs free energy of mixing, Delta G(mix) < 0) or suspensions (Delta G(mix) > 0) depending on their thermodynamic stability. Small objects tend to form solutions, larger ones suspension ...

WILEY2022