Publications de Guillaume André Jean Fraux

PLUMED Tutorials: A collaborative, community-driven learning ecosystem

Michele Ceriotti, Guillaume André Jean Fraux

In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several cha ...

AIP Publishing2025

i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations

Michele Ceriotti, Guillaume André Jean Fraux

Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach ...

2024

Surface segregation in high-entropy alloys from alchemical machine learning

Michele Ceriotti, Sandip De, Nataliya Lopanitsyna, Guillaume André Jean Fraux, Arslan Mazitov

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Iop Publishing Ltd2024

Fast evaluation of spherical harmonics with sphericart

Michele Ceriotti, Guillaume André Jean Fraux, Filippo Bigi

Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted basis to expand functions on a sphere, and they are used routinely in physical and theoretical chemistry as well as in different fields of science and technology, from geology and atmos ...

AIP Publishing2023

Modeling high-entropy transition metal alloys with alchemical compression

Michele Ceriotti, Sandip De, Nataliya Lopanitsyna, Guillaume André Jean Fraux

Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in cat ...

AMER PHYSICAL SOC2023

Unified theory of atom-centered representations and message-passing machine-learning schemes

Michele Ceriotti, Jigyasa Nigam, Guillaume André Jean Fraux, Sergey Pozdnyakov

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-cen ...

AIP Publishing2022

Efficient implementation of atom-density representations

Till Junge, Félix Benedito Clément Musil, Michele Ceriotti, Michael John Willatt, Max David Veit, Alexander Jan Goscinski, Guillaume André Jean Fraux

Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materi ...

2021

The role of feature space in atomistic learning

Michele Ceriotti, Giulio Imbalzano, Alexander Jan Goscinski, Guillaume André Jean Fraux

Efficient, physically-inspired descriptors of the structure and composition of molecules and materials play a key role in the application of machine-learning techniques to atomistic simulations. The proliferation of approaches, as well as the fact that eac ...

2021

Chemiscope: interactive structure-property explorer for materials and molecules

Michele Ceriotti, Guillaume André Jean Fraux

The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous. Computational chemistry can be used to generate databases containing billions of potential struct ...

2020

Structure-property maps with Kernel principal covariates regression

Michele Ceriotti, Benjamin Aaron Helfrecht, Guillaume André Jean Fraux

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PC ...

2020

DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening

Guillaume André Jean Fraux

Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking data sets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the ...

2020

Fermi resonance in CO2: Mode assignment and quantum nuclear effects from first principles molecular dynamics

Sara Bonella, Guillaume André Jean Fraux

Vibrational spectroscopy is a fundamental tool to investigate local atomic arrangements and the effect of the environment, provided that the spectral features can be correctly assigned. This can be challenging in experiments and simulations when double pea ...

Amer Inst Physics2017