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Biomolecular Simulation: a Perspective from High Performance Computing

Publications associées (40)

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Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

EPFL2024

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

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

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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

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

Extreme Scalability of DFT-Based QM/MM MD Simulations Using MiMiC

Ursula Röthlisberger, Simone Meloni, Martin Peter Bircher, Viacheslav Bolnykh, Jógvan Magnus Olsen, Paolo Carloni

We present a highly scalable DFT-based QM/MM implementation developed within MiMiC, a recently introduced multiscale modeling framework that uses a loose-coupling strategy in conjunction with a multiple-program multiple-data (MPMD) approach. The computatio ...

2019

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We present a flexible and efficient framework for multiscale modeling in computational chemistry (MiMiC). It is based on a multiple-program multiple-data (MPMD) model with loosely coupled programs. Fast data exchange between programs is achieved through th ...

AMER CHEMICAL SOC2019

The Role of Post-translational Modifications on the Energy Landscape of Huntingtin N-Terminus

Hilal Lashuel, Charlotte Julie Caroline Gehin

Huntington disease is a neurodegenerative disease characterized by a polymorphic tract of polyglutamine repeats in exon 1 of the huntingtin protein, which is thought to be responsible for protein aggregation and neuronal death. The polyglutamine tract is p ...

2019

cemff: A force field database for cementitious materials including validations, applications and opportunities

Paul Bowen, Sandra Caroline Galmarini, Aslam Kunhi Mohamed, David Geissbühler

This paper reviews atomistic force field parameterizations for molecular simulations of cementitious minerals, such as tricalcium silicate (C3S), portlandite (CH), tobermorites (model C-S-H). Computational techniques applied to these materials include clas ...

Elsevier2017

Classical Force Fields Tailored for QM Applications: Is It Really a Feasible Strategy?

Oliviero Andreussi

Classical molecular dynamics is more and more often coupled to quantum mechanical based techniques as a statistical tool to sample configurations of molecular systems embedded in complex environments. Nonetheless, the classical potentials describing the mo ...

Amer Chemical Soc2017