Publications related to Improved United-Atom Force Field for 1-Alkyl-3-methylimidazolium Chloride

Structure and dynamics of liquid water from ab initio simulations: adding Minnesota density functionals to Jacob's ladder

Ursula Röthlisberger, Justin Villard, Martin Peter Bircher

The accurate representation of the structural and dynamical properties of water is essential for simulating the unique behavior of this ubiquitous solvent. Here we assess the current status of describing liquid water using ab initio molecular dynamics, wit ...

Cambridge2024

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

Advancing first principle-based molecular dynamics of biological systems with machine learning

François Louis Mouvet

Molecular dynamics (MD) simulations have emerged as a transformative approach to analyse molecular systems at the atomic level, offering valuable insights into complex biological processes. Many biological phenomena can only accurately be described by inco ...

EPFL2023

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

Francesco Aquilante

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments sp ...

Washington2023

Low-Temperature Dynamics of Water Confined in Unidirectional Hydrophilic Zeolite Nanopores

Livia Eleonora Bove Kado, Umbertoluca Ranieri, Maria Rescigno

The dynamical properties of water molecules confinedin the unidirectionalhydrophilic nanopores of AlPO4-54 are investigated withquasi-elastic neutron scattering as a function of temperature downto 118 K. AlPO4-54 has among the largest pores known foralumin ...

AMER CHEMICAL SOC2023

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

Berend Smit, Senja Dominique Barthel, Amber Kashan Mace

We present an efficient method to compute diffusion coefficients of multiparticle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion ...

Washington2023

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

Mixing and diffusion for rough shear flows

Maria Colombo, Klaus Martin Widmayer

This article addresses mixing and diffusion properties of passive scalars advected by rough (

C^\alpha

) shear flows. We show that in general, one cannot expect a rough shear flow to increase the rate of inviscid mixing to more than that of a smooth shear ...

2021

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Michele Ceriotti, Fabio Pietrucci

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from t ...

AMER CHEMICAL SOC2020

Improved United-Atom Force Field for 1-Alkyl-3-methylimidazolium Chloride

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Structure and dynamics of liquid water from ab initio simulations: adding Minnesota density functionals to Jacob's ladder

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Advancing first principle-based molecular dynamics of biological systems with machine learning

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

Low-Temperature Dynamics of Water Confined in Unidirectional Hydrophilic Zeolite Nanopores

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

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

A general and efficient framework for atomistic machine learning

Mixing and diffusion for rough shear flows

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Low-Temperature Dynamics of Water Confined in Unidirectional Hydrophilic Zeolite Nanopores

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

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

A general and efficient framework for atomistic machine learning

Mixing and diffusion for rough shear flows

Structure and dynamics of liquid water from ab initio simulations: adding Minnesota density functionals to Jacob's ladder

Advancing first principle-based molecular dynamics of biological systems with machine learning

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes