Guillaume André Jean Fraux

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Guillaume André Jean Fraux

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Structure-property maps with Kernel principal covariates regression

Fast evaluation of spherical harmonics with sphericart

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

PLUMED Tutorials: A collaborative, community-driven learning ecosystem

Surface segregation in high-entropy alloys from alchemical machine learning

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

Efficient implementation of atom-density representations

The role of feature space in atomistic learning

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

Modeling high-entropy transition metal alloys with alchemical compression