Stability of helium bubbles in alpha-iron: A molecular dynamics study

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Publications associées (32)

Beyond potentials: Integrated machine learning models for materials

Michele Ceriotti

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

SPRINGER HEIDELBERG2022

Real space and reciprocal space investigations of the spin dynamics in skyrmion-hosting materials

Le Yu

Magnetic skyrmions are whirl-like spin configurations with particle-like properties protected by non-trivial topology. Due to their unique spin structures and dynamical properties, they have attracted tremendous interests over the past decade, from fundame ...

EPFL2022

cell2mol: encoding chemistry to interpret crystallographic data

Anne-Clémence Corminboeuf, Sergio Vela Llausi, Yuri Cho, Rubén Laplaza Solanas

The creation and maintenance of crystallographic data repositories is one of the greatest data-related achievements in chemistry. Platforms such as the Cambridge Structural Database host what is likely the most diverse collection of synthesizable molecules ...

NATURE PORTFOLIO2022

Predicting hot-electron free energies from ground-state data

Michele Ceriotti, Chiheb Ben Mahmoud, Federico Grasselli

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that i ...

AMER PHYSICAL SOC2022

Microscopic picture of paraelectric perovskites from structural prototypes

Nicola Marzari, Giovanni Pizzi, Boris Kozinsky, Michele Kotiuga

We highlight with first-principles molecular dynamics the persistence of intrinsic < 111 > Ti off-centerings for BaTiO3 in its cubic paraelectric phase. Intriguingly, these are inconsistent with the Pm (3) over barm space group often used to atomistically ...

AMER PHYSICAL SOC2022

Effects of surface rigidity and metallicity on dielectric properties and ion interactions at aqueous hydrophobic interfaces

Philip Robin Loche

Using classical molecular dynamics simulations, we investigate the dielectric properties at interfaces of water with graphene, graphite, hexane, and water vapor. For graphite, we compare metallic and nonmetallic versions. At the vapor-liquid water and hexa ...

AIP Publishing2022

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

The Pristine survey - XVI. The metallicity of 26 stellar streams around the Milky Way detected with the STREAMFINDER in Gaia EDR3

Pascale Jablonka, Carmela Lardo

We use the photometric metallicities provided by the panoramic Pristine survey to study the veracity and derive the metallicities of the numerous stellar streams found by the application of the STREAMFINDER algorithm to the Gaia Early Data Release 3 data. ...

OXFORD UNIV PRESS2022

In situ work function measurements of W, WO3 nanostructured surfaces

Francisco Sanchez

Surface nanostructuring enables the fabrication of materials with highly desirable properties. Nanostructured tungsten surfaces have potential applications in solar water splitting. Exposing a polished tungsten surface to helium plasma induces various surf ...

ELSEVIER SCIENCE SA2022

Transferable machine-learning models of complex materials: the case of GaAs

Giulio Imbalzano

Molecular simulations allow to investigate the behaviour of materials at the atomistic level, shedding light on phenomena that cannot be directly observed in experiments. Accurate results can be obtained with ab initio methods, while simulations of large-s ...

EPFL2021

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

Ultrafast Resonant Interatomic Coulombic Decay Induced by Quantum Fluid Dynamics

Marcel Drabbels, Daniela Rupp, Andrew Daniel Bonnar Clark, Verónica Oliver Álvarez de Lara

Interatomic processes play a crucial role in weakly bound complexes exposed to ionizing radiation; therefore, gaining a thorough understanding of their efficiency is of fundamental importance. Here, we directly measure the timescale of interatomic Coulombi ...

American Physical Society2021

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