Publications associées à A combined on-the-fly/interpolation procedure for evaluating energy values needed in molecular simulations

Machine learning-aided generative molecular design

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Exciton migration in two-dimensional materials

Excitons play an essential role in the optical response of two-dimensional materials. These are bound states showing up in the band gaps of many-body systems and are conceived as quasiparticles formed by an electron and a hole. By performing real-time simu ...

Nature Portfolio2024

2023 Roadmap on molecular modelling of electrochemical energy materials

Alfredo Pasquarello, Chunmin Zhang, Assil Bouzid

New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as em ...

Iop Publishing Ltd2023

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

Fabio Pietrucci, Sara Bonella, 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

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

Universal machine learning for the response of atomistic systems to external fields

Yaolong Zhang

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...

Nature Portfolio2023

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

Factorized structure of the long-range two-electron integrals tensor and its application in quantum chemistry

Laura Grigori

We introduce two new approximation methods for the numerical evaluation of the long-range component of the range-separated Coulomb potential and the approximation of the resulting high dimensional Two-Electron Integrals tensor (TEI) with long-range interac ...

Academic Press Inc Elsevier Science2023

A Full-Wave Reference Simulator for Computing Surface Reflectance

Mengqi Xia

Computing light reflection from rough surfaces is an important topic in computer graphics. Reflection models developed based on geometric optics fail to capture wave effects such as diffraction and interference, while existing models based on physical opti ...

ASSOC COMPUTING MACHINERY2023

Stochastic adaptive resampling for the estimation of discrete choice models

Michel Bierlaire, Nicola Marco Ortelli, Matthieu Marie Cochon de Lapparent

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

2023

Molecular simulation on two-dimensional nanoporous material for energy-efficient separation

Mohammad Tohidivahdat

The energy-consumption by the chemical and petrochemical industry in the European Union (EU) accounts for about quarter of its energy footprint. Approximately half of this energy goes toward chemical separation, currently dominated by thermally-driven pro ...

EPFL2023

Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

Edoardo Remelli

In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pr ...

EPFL2022

Effects of Degrees of Freedom on Calculating Diffusion Properties in Nanoporous Materials br

Berend Smit, Henglu Xu, Raffaela Cabriolu

If one carries out a molecular simulation ofNparticlesusing periodic boundary conditions, linear momentum is conserved,and hence, the number of degrees of freedom is set to 3N-3. Inmost programs, this number of degrees of freedom is the defaultsetting. How ...

AMER CHEMICAL SOC2022

Adhesive wear with a coarse-grained discrete element model

Jean-François Molinari, Son-Jonathan Pham-Ba

The use of molecular dynamics (MD) simulations has led to promising results to unravel the atomistic origins of adhesive wear, and in particular for the onset of wear at nanoscale surface asperities. However, MD simulations come with a high computational c ...

ELSEVIER SCIENCE SA2022

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Sundar Subramaniam Ganesh

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

EPFL2022

Efficient and Accurate Physically-Based Differentiable Rendering

Delio Aleardo Vicini

Physically-based rendering algorithms generate photorealistic images of virtual scenes. By simulating light paths in a scene, complex physical effects such as shadows, reflections and volumetric scattering can be reproduced. Over the last decade, physicall ...

EPFL2022

Single photon detection with amorphous silicon-based microchannel plates: A Monte Carlo model

Christophe Ballif, Nicolas Würsch, Jonathan Emanuel Thomet, Janina Christine Isabelle Löffler, Samira Alexandra Frey

Microchannel plates based on amorphous silicon have the inherent capability to overcome the existing spatial and temporal resolution limitations of single photon detectors. A Monte Carlo model-based study is presented here that serves to exploit their full ...

2022

Differentiable Signed Distance Function Rendering

Sébastien Nicolas Speierer, Wenzel Alban Jakob, Delio Aleardo Vicini

Physically-based differentiable rendering has recently emerged as an attractive new technique for solving inverse problems that recover complete 3D scene representations from images. The inversion of shape parameters is of particular interest but also pose ...

ASSOC COMPUTING MACHINERY2022

Differentiable Physically Based Rendering: Algorithms, Systems and Applications

Merlin Eléazar Nimier-David

Physically based rendering methods can create photorealistic images by simulating the propagation and interaction of light in a virtual scene. Given a scene description including the shape of objects, participating media, material properties, etc., the sim ...

EPFL2022