Publications by Félix Benedito Clément Musil

Optimal radial basis for density-based atomic representations

Félix Benedito Clément Musil, Michele Ceriotti, Alexander Jan Goscinski, Jigyasa Nigam, Sergey Pozdnyakov

The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations ...

2021

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

Physics-Inspired Structural Representations for Molecules and Materials

Félix Benedito Clément Musil, Michele Ceriotti, Andrea Grisafi

The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atom ...

AMER CHEMICAL SOC2021

A general and efficient framework for atomistic machine learning

Félix Benedito Clément Musil

Over the last two decades, many technological and scientific discoveries, ranging from the development of materials for energy conversion and storage through the design of new drugs, have been accelerated by the use of preliminary in silico experiments, to ...

EPFL2021

Atom-density representations for machine learning

Félix Benedito Clément Musil, Michele Ceriotti, Michael John Willatt

This has led to a proliferation of alternative ways to convert an atomic structure into an input for a machine-learning model. We introduce an abstract definition of chemical environments that is based on a smoothed atomic density, using a bra-ket notation ...

2019

Machine Learning at the Atomic Scale

Félix Benedito Clément Musil, Michele Ceriotti

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...

SWISS CHEMICAL SOC2019

Data Science Based Mg Corrosion Engineering

Félix Benedito Clément Musil, Robert Horst Meissner

Magnesium exhibits a high potential for a variety of applications in areas such as transport, energy and medicine. However, untreated magnesium alloys are prone to corrosion, restricting their practical application. Therefore, it is necessary to develop ne ...

2019

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Félix Benedito Clément Musil, Michele Ceriotti, Mikhail Langovoy, Michael John Willatt

We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated ba ...

2019

Chemical shifts in molecular solids by machine learning

Félix Benedito Clément Musil, Michele Ceriotti, Maxime Lagier, David Lyndon Emsley, Sandip De, Albert Hofstetter, Federico Maria Paruzzo

Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the abi ...

Nature Research2018

Machine learning for the structure–energy–property landscapes of molecular crystals

Félix Benedito Clément Musil, Michele Ceriotti, Sandip De

2018

Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

Félix Benedito Clément Musil, Michele Ceriotti, Michael John Willatt

Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes it possible to im ...

2018

Mapping and classifying molecules from a high-throughput structural database

Félix Benedito Clément Musil, Michele Ceriotti, Sandip De, Teresa Ingram, Carsten Baldauf

High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational search ...

Biomed Central Ltd2017