Publications associées à Molecular dynamics nano-scratching of aluminium: a novel quantitative energy-based analysis method

Publications associées (31)

Effects of Rate-dependent Plasticity on Residual Stresses in Additive Manufacturing of Complex Alloys

The recent introduction of high entropy alloys presents an exciting opportunity for novel material performance, offering extensive possibilities for tailoring material properties. Dislocation mechanisms in these alloys must be fully understood since they p ...

EPFL2025

An Investigation of VO2 Nanowire Arrays for Integrated Sensing. From Non-Stochastic Nanowires to Stochastic Nanostructures

Andrea Iaconeta, Mihai Adrian Ionescu, Andras Kis, Igor Stolichnov, Anna Varini, Cyrille Masserey, Vanessa Conti, Riccardo Chiesa

This paper presents a novel nanoscale platform based on vanadium dioxide (VO2) two-terminal devices shaped like nanowires (NWs). For the first time we report CMOS compatible VO2 two-terminal devices with little-to-no stochasticity in the absence of electro ...

IEEE2025

Solvation Free Energies from Machine Learning Molecular Dynamics

Nicola Marzari, Nicéphore Arthur François Bonnet

The present work proposes an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approa ...

Amer Chemical Soc2024

Teacher-Mediated and Student-Led Interaction with a Physics Simulation: Effects on the Learning Experience

Denis Gillet, Juan Carlos Farah, Jérémy Alain La Scala-Jayet

Computer simulations are often used as support material for science education, as they can engage students through inquiry-based learning, promote their active interaction in the experimentation phase, and help them visualize abstract concepts. For instanc ...

2024

Thermal transport of glasses via machine learning driven simulations

Federico Grasselli, Paolo Pegolo

Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...

Frontiers Media Sa2024

Exciton migration in two-dimensional materials

Giovanni Consalvo Cistaro

Excitons play an essential role in the optical response of two-dimensional materials. These are bound states showing up in the band gaps of many-body systems and are conceived as quasiparticles formed by an electron and a hole. By performing real-time simu ...

Nature Portfolio2024

Bridging Monte Carlo worlds: a new framework for accelerator simulations applied on shielding and machine protection studies

André Donadon Servelle

This thesis delves into the critical study of particle transport in matter, particularly emphasising its implications for machine protection at CERN's accelerator complex and facilities. To conduct studies of this nature, FLUKA and Geant4 stand out within ...

EPFL2024

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Justin Villard

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

EPFL2023

Adhesive wear with a coarse-grained discrete element model

Jean-François Molinari, Son-Jonathan Pham-Ba

The use of molecular dynamics (MD) simulations has led to promising results to unravel the atomistic origins of adhesive wear, and in particular for the onset of wear at nanoscale surface asperities. However, MD simulations come with a high computational c ...

ELSEVIER SCIENCE SA2022

Visualization, Interactive Handling and Simulation of Molecules in Commodity Augmented Reality in Web Browsers Using moleculARweb's Virtual Modeling Kits

Matteo Dal Peraro, Luciano Andres Abriata, Lucien Fabrice Krapp, Fabio Jose Cortes Rodriguez

moleculARweb (https://molecularweb.epfl.ch) began as a website for education and outreach in chemistry and structural biology through augmented reality (AR) content that runs in the web browsers of regular devices like smartphones, tablets, and computers. ...

SWISS CHEMICAL SOC2022

Computational Studies of the Proton-Coupled Metal Ion Transport in the SLC11/NRAMP Family of Transporters

Maria Letizia Merlini

In the last years, it has been demonstrated a link between the overload of metal ions inside nervous system cells and the onset of severe neurodegenerative diseases. This prompted the investigation of the structural and functional properties of transporter ...

EPFL2022

Understanding the mechanisms of adhesive wear for heterogeneous materials through atomistic simulations

Jean-François Molinari, Antonio Joaquin Garcia Suarez, Sacha Zenon Wattel

When two rough surfaces slide against each other, two main behaviors can be observed at the asperity level: A ductile behavior where the asperities tend to smooth out or a brittle behavior where the asperities detach and form debris. Recently, a critical l ...

2022

A general and efficient framework for atomistic machine learning

Félix Benedito Clément Musil

Over the last two decades, many technological and scientific discoveries, ranging from the development of materials for energy conversion and storage through the design of new drugs, have been accelerated by the use of preliminary in silico experiments, to ...

EPFL2021

Self-consistent simulation of multi-component plasma turbulence and neutral dynamics in the tokamak boundary

André Calado Coroado

The interaction between neutral particles and the plasma plays a key role in determining the dynamics of the tokamak boundary that, in turn, significantly impact the overall performance of the device. Leveraging the work in Wersal and Ricci [Nucl. Fusion 5 ...

EPFL2021

Uncertainty estimation for molecular dynamics and sampling

Yongbin Zhuang, Michele Ceriotti, Kevin Rossi, Venkat Kapil, Federico Grasselli, Giulio Imbalzano, Edgar Albert Engel

Machine-learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale, and complexity. Given the interpolative nature of these models, the r ...

2021

Reduced-order Models for Simulating Coupled Geometric Instabilities in Steel Beam-columns Under Inelastic Cyclic Straining

Alexander Riley Hartloper

Numerical models that can simulate coupled geometric instabilities in steel beam-columns are required for the seismic assessment of certain steel moment-resisting frames. Reliably simulating coupled instabilities requires that the steel material is precise ...

EPFL2021

Rapid screening of nanopore candidates in nanoporous single-layer graphene for selective separations using molecular visualization and interatomic potentials

Luc Sébastien Bondaz

Nanoporous single-layer graphene is promising as an ideal membrane because of its extreme thinness, chemical resistance, and mechanical strength, provided that selective nanopores are successfully incorporated. However, screening and understanding the tran ...

AIP Publishing2021

Bias free multiobjective active learning for materials design and discovery

Berend Smit, Kevin Maik Jablonka, Shi-Lin Wang

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the se ...

Nature Research2021

Variational phase-field continuum model uncovers adhesive wear mechanisms in asperity junctions

Jean-François Molinari, Sylvain Collet

Wear is well known for causing material loss in a sliding interface. Available macroscopic approaches are bound to empirical fitting parameters, which range several orders of magnitude. Major advances in tribology have recently been achieved via Molecular ...

2020

Gaussian Process Regression for Materials and Molecules

Michele Ceriotti, David Mark Wilkins

We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...

AMER CHEMICAL SOC2021