Guillaume André Jean Fraux

Related publications (7)

Modeling high-entropy transition metal alloys with alchemical compression

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

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 catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort, and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semiquantitative accuracy for prototypical alloys and is remarkably stable when extrapolating to structures outside its training set. We use this framework to study element segregation in a computational experiment that simulates an equimolar alloy of all 25 elements, mimicking the seminal experiments in the groups of Yeh and Cantor, and use our observations on the short-range order relations between the elements to define a data-driven set of Hume-Rothery rules that can serve as guidance for alloy design. We conclude with a study of three prototypical alloys, CoCrFeMnNi, CoCrFeMoNi, and IrPdPtRhRu, determining their stability and the short-range order behavior of their constituents.

AMER PHYSICAL SOC2023

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

Michele Ceriotti, Guillaume André Jean Fraux, Jigyasa Nigam, 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-centered environments, which are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), which are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes that gather information on the relationship between neighboring atoms using "message-passing " ideas cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and provide a coherent foundation to systematize our understanding of both atom-centered and message-passing and invariant and equivariant machine-learning schemes. Published under an exclusive license by AIP Publishing.

AIP Publishing2022

Efficient implementation of atom-density representations

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

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 materials and molecules and to explore and visualize their chemical structures and compositions. Recently, it has become clear that many of the most effective representations share a fundamental formal connection. They can all be expressed as a discretization of n-body correlation functions of the local atom density, suggesting the opportunity of standardizing and, more importantly, optimizing their evaluation. We present an implementation, named librascal, whose modular design lends itself both to developing refinements to the density-based formalism and to rapid prototyping for new developments of rotationally equivariant atomistic representations. As an example, we discuss smooth overlap of atomic position (SOAP) features, perhaps the most widely used member of this family of representations, to show how the expansion of the local density can be optimized for any choice of radial basis sets. We discuss the representation in the context of a kernel ridge regression model, commonly used with SOAP features, and analyze how the computational effort scales for each of the individual steps of the calculation. By applying data reduction techniques in feature space, we show how to reduce the total computational cost by a factor of up to 4 without affecting the model’s symmetry properties and without significantly impacting its accuracy.

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 structures (Ruddigkeit, Deursen, Blum, & Reymond, 2012), and predict some of the associated properties (Montavon et al., 2013; Ramakrishnan, Dral, Rupp, & Lilienfeld, 2014). Unfortunately, the very large number of structures makes exploring such database — to understand structure-property relations or find the best structure for a given application — a daunting task. In recent years, multiple molecular representations(Bartók, Kondor, & Csányi, 2013; Behler &Parrinello, 2007; Willatt, Musil, & Ceriotti, 2019) have been developed to compute structural similarities between materials or molecules, incorporating physically-relevant information and symmetries. The features associated with these representations can be used for unsupervised machine learning applications, such as clustering or classification of the different structures,and high-through put screening of database for specific properties (De, Musil, Ingram, Baldauf,& Ceriotti, 2017; Hautier, 2019; Maier, Stöwe, & Sieg, 2007). Unfortunately, the dimensionality of these features (as well as most of other descriptors used in chemical and materials informatics) is very high, which makes the resulting classifications, clustering or mapping very hard to visualize. Dimensionality reduction algorithms (Ceriotti, Tribello, & Parrinello, 2011;McInnes, Healy, & Melville, 2018; Schölkopf, Smola, & Müller, 1998) can reduce the number of relevant dimensions to a handful, creating 2D or 3D maps of the full database.

2020

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 virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly create benchmarking sets using the protein databank. DUBS uses a simple input text based format along with the Lemon data mining framework to efficiently access and organize data to the protein databank and output commonly used inputs for virtual screening software. The simple input format used by DUBS allows users to define their own benchmarking data sets and access the corresponding information directly from the software package. Currently, it only takes DUBS less than 2 min to create a benchmark using this format. Since DUBS uses a simple python script, users can easily modify this to create more complex benchmarks. We hope that DUBS will be a useful community resource to provide a standardized representation for benchmarking data sets in virtual screening. The DUBS package is available on GitHub at https://github.com/chopralab/lemon/tree/master/dubs.

2020

Structure-property maps with Kernel principal covariates regression

Michele Ceriotti, Guillaume André Jean Fraux, Benjamin Aaron Helfrecht

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 (PCovR) is an underappreciated method that interpolates between principal component analysis and linear regression and can be used conveniently to reveal structure-property relations in terms of simple-to-interpret, low-dimensional maps. Here we provide a pedagogic overview of these data analysis schemes, including the use of the kernel trick to introduce an element of non-linearity while maintaining most of the convenience and the simplicity of linear approaches. We then introduce a kernelized version of PCovR and a sparsified extension, and demonstrate the performance of this approach in revealing and predicting structure-property relations in chemistry and materials science, showing a variety of examples including elemental carbon, porous silicate frameworks, organic molecules, amino acid conformers, and molecular materials.

2020

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 peaks are present because they can have different origins. Fermi dyads are a common class of such doublets, stemming from the resonance of the fundamental excitation of a mode with the overtone of another. We present a new, efficient approach to unambiguously characterize Fermi resonances in density functional theory (DFT) based simulations of condensed phase systems. With it, the spectral features can be assigned and the two resonating modes identified. We also show how data from DFT simulations employing classical nuclear dynamics can be post-processed and combined with a perturbative quantum treatment at a finite temperature to include analytically thermal quantum nuclear effects. The inclusion of these effects is crucial to correct some of the qualitative failures of the Newtonian dynamics simulations at a low temperature such as, in particular, the behavior of the frequency splitting of the Fermi dyad. We show, by comparing with experimental data for the paradigmatic case of supercritical CO2, that these thermal quantum effects can be substantial even at ambient conditions and that our scheme provides an accurate and computationally convenient approach to account for them. Published by AIP Publishing.

Amer Inst Physics2017