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

Publications associées (43)

Efficient and insightful descriptors for representing molecular and material space

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

EPFL2024

Towards a multiscale point cloud structural similarity metric

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Point clouds are effective data structures for the rep- resentation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point c ...

2023

Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties

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Symmetry considerations are at the core of the major frameworks used to provide an effective mathematical representation of atomic configurations that is then used in machine-learning models to predict the properties associated with each structure. In most ...

AIP Publishing2022

A smooth basis for atomistic machine learning

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Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighborhood of each atom in the system. Symmetry considerations support the use of spherical harmonics to ...

AIP Publishing2022

Nuclear Excitation by Free Muon Capture

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Efficient excitation of nuclei via exchange of a real or virtual photon has a fundamental importance for nuclear science and technology development. Here, we present a mechanism of nuclear excitation based on the capture of a free muon into the atomic orbi ...

AMER PHYSICAL SOC2022

Optimal radial basis for density-based atomic representations

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

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 ...

AIP Publishing2021

Mapping Orbital-Resolved Magnetism in Single Lanthanide Atoms

Harald Brune, Stefano Rusponi, Marina Pivetta, Fabio Donati, Aparajita Singha

Single lanthanide atoms and molecules are promising candidates for atomic data storage and quantum logic due to the long lifetime of their magnetic quantum states. Accessing and controlling these states through electrical transport requires precise knowled ...

AMER CHEMICAL SOC2021

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Atomistic simulations provide insights into structure-property relations on an atomic size and length scale, that are complementary to the macroscopic observables that can be obtained from experiments. Quantitative predictions, however, are usually hindere ...

2021

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Michele Ceriotti, Michael John Willatt

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear fun ...

Springer International Publishing2020

Incompleteness of Atomic Structure Representations

Michele Ceriotti, Sergey Pozdnyakov, Michael John Willatt

Many-body descriptors are widely used to represent atomic environments in the construction of machine-learned interatomic potentials and more broadly for fitting, classification, and embedding tasks on atomic structures. There is a widespread belief in the ...

AMER PHYSICAL SOC2020

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