Publications de Philip Robin Loche

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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 ...

Although conceptually simple, the air-water interface displays rich behavior and is subject to intense experimental and theoretical investigations. Different definitions of the electrostatic surface potential as well as different calculation methods, each ...

Although important for atmospheric processes and gas-phase catalysis, very little is known about the hydration state of ions in the vapor phase. Here we study the evaporation energetics and kinetics of a chloride ion from liquid water by molecular dynamics ...

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrost ...

Seeded emulsion polymerization is one of the best-known methods for preparing polymer particles with a controlled size, composition, and shape. It first requires the preparation of seed particles, which are then swollen with additional monomer (the same as ...

From the stability of colloidal suspensions to the charging of electrodes, electric double layers play a pivotal role in aqueous systems. The interactions between interfaces, water molecules, ions and other solutes making up the electrical double layer spa ...

Despite the ubiquity of aqueous electrolytes, the effect of salt on water organization remains controversial. We introduce a nonlocal and nonlinear field theory for the nanoscale polarization of ions and water and derive the electrolyte dielectric response ...

Water at negative pressures can remain in a metastable state for a surprisingly long time before it reaches equilibrium by cavitation, i.e., by the formation of vapor bubbles. The wide spread of experimentally measured cavitation pressures depending on wat ...

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

Most atomistic machine learning (ML) models rely on a locality ansatz and decompose the energy into a sum of short-ranged, atom-centered contributions. This leads to clear limitations when trying to describe problems that are dominated by long-range physic ...

Philip Robin Loche

Chattez avec Graph Search

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

Electrokinetic, electrochemical, and electrostatic surface potentials of the pristine water liquid-vapor interface

Molecular dynamics simulations of the evaporation of hydrated ions from aqueous solution

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Revealing the Formation Dynamics of Janus Polymer Particles: Insights from Experiments and Molecular Dynamics

Multiscale Modeling of Aqueous Electric Double Layers

Dielectric Properties of Aqueous Electrolytes at the Nanoscale

Water cavitation results from the kinetic competition of bulk, surface, and surface-defect nucleation events

Prediction rigidities for data-driven chemistry

Fast and flexible long-range models for atomistic machine learning