Publications associées à Implicit solvation

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

A continuum approximation approach to the depot location problem in a crowd-shipping system

Nikolaos Geroliminis, Patrick Stefan Adriaan Stokkink

Last-mile delivery in the logistics chain contributes to congestion in urban networks due to frequent stops. Crowd-shipping is a sustainable and low-cost alternative to traditional delivery but relies heavily on the availability of occasional couriers. In ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Effects of surface wettability on (001)-WO3 and (100)-WSe2: A spin-polarized DFT-MD study

Kevin Sivula, Rangsiman Ketkaew

An extensive understanding of WO3 and WSe2 bulk crystalline structures and explicit solvent effects on (001)-WO3 and (100)-WSe2 facets are essential for design of efficient (photo) electrocatalysts. The atomistic level understanding of both WO(3 )and WSe2 ...

ELSEVIER2022

Modeling non-covalent interactions in condensed phase

Veronika Juraskova

The modeling of non-covalent interactions, solvation effects, and chemical reactions in complex molecular environment is a challenging task. Current state-of-the-art approaches often rely on static computations using implicit solvent models and harmonic ap ...

EPFL2022

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

High-Throughput Nanopore Fabrication and Classification Using Xe-Ion Irradiation and Automated Pore-Edge Analysis

Aleksandra Radenovic, Andras Kis, Martina Lihter, Mukesh Kumar Tripathi, Mukeshchand Thakur, Sanjin Marion, Michal Daniel Macha

Large-area nanopore drilling is a major bottleneck in state-of-the-art nanoporous 2D membrane fabrication protocols. In addition, high-quality structural and statistical descriptions of as-fabricated porous membranes are key to predicting the corresponding ...

AMER CHEMICAL SOC2022

On the durability of surgical masks after simulated handling and wear

Véronique Michaud, Vincent Marc Varanges, Jian Wang, Baris Çaglar, Yann Albert Gérard Lebaupin, René M. Rossi

After the spread of COVID-19, surgical masks became highly recommended to the public. They tend to be handled and used multiple times, which may impact their performance. To evaluate this risk, surgical masks of Type IIR were submitted to four simulated tr ...

Nature Research2022

Machine Learning-Guided Approach for Studying Solvation Environments

Sandip De, Yasemin Basdogan

Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, we report a static and all-quantum ...

AMER CHEMICAL SOC2020

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

Michele Ceriotti, Anne-Clémence Corminboeuf, Kevin Rossi, Veronika Juraskova

We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges ...

AMER CHEMICAL SOC2020

Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry

Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat, Daniel Hollas

This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible medium-size organic molecules at high ab initio level. We offer a modular envir ...

AMER CHEMICAL SOC2020