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Publications associées (31)

The role of Zn in C-S-H atomic structure, nucleation and growth

Anna Morales Melgares

The most promising solution towards cementitious materials with a lower carbon footprint is the partial substitution of the clinker by supplementary cementitious materials (SCMs) such as fly ash, blast furnace slag, limestone and calcined clays. The produc ...

EPFL2023

Multiphysics model for assessing photoelectrochemical phenomena under concentrated irradiation

Sophia Haussener, Isaac Thomas Holmes-Gentle, Franky Esteban Bedoya Lora

A multiphysics model was developed for a photoelectrochemical (PEC) cell at the device level to simulate water splitting operating under concentrated irradiation (between 50 to 600 kW m−2). The 2D model couples charge, heat, mass, photon, and momentum tran ...

2023

Robustness of Local Predictions in Atomistic Machine Learning Models

Michele Ceriotti, Sanggyu Chong, Federico Grasselli, Chiheb Ben Mahmoud

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Amer Chemical Soc2023

Nanomechanical Modeling of the Bending Response of Silicon Nanowires

Yusuf Leblebici, Zuhal Tasdemir, Mohammad Nasr Esfahani, Mustafa Yilmaz

Understanding the mechanical behavior of silicon nanowiresis essentialfor the implementation of advanced nanoscale devices. Although bendingtests are predominantly used for this purpose, their findings shouldbe properly interpreted through modeling. Variou ...

AMER CHEMICAL SOC2023

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models

Sara Bonella, Fabio Pietrucci, David Daniel Girardier

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic r ...

Aip Publishing2023

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

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

Federico Grasselli

Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic co ...

NATURE PORTFOLIO2022

Neural Network Potentials for Age Hardening Aluminum Alloys

Daniel John Gilles Marchand

High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during ...

EPFL2022

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

Roadmap on Machine learning in electronic structure

Michele Ceriotti, Jigyasa Nigam, Andrea Grisafi, Max David Veit

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by ...

IOP Publishing Ltd2022

Assessing the persistence of chalcogen bonds in solution with neural network potentials

Rubén Laplaza Solanas, Anne-Clémence Corminboeuf, Frédéric Célerse, Veronika Juraskova

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environme ...

2022

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