Publications by Sergey Pozdnyakov

Advancing understanding and practical performance of machine learning interatomic potentials

Atomistic machine learning has revolutionized atomistic modeling. Traditionally, molecular dynamics simulations relied on classical force fields or electronic structure calculations like Density Functional Theory. Classical force fields are fast but less p ...

EPFL2025

Completeness of Atomic Structure Representations

Michele Ceriotti, Jigyasa Nigam, Sergey Pozdnyakov, Kevin Kazuki Huguenin-Dumittan

In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. The problem has become even more important with ...

AIP Publishing2024

Probing the effects of broken symmetries in machine learning

Michele Ceriotti, Sergey Pozdnyakov

Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properti ...

2024

Wigner kernels: Body-ordered equivariant machine learning without a basis

Michele Ceriotti, Sergey Pozdnyakov, Filippo Bigi

Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approaches that h ...

AIP Publishing2024

Incompleteness of graph neural networks for points clouds in three dimensions

Michele Ceriotti, Sergey Pozdnyakov

Graph neural networks (GNN) are very popular methods in machine learning and have been applied very successfully to the prediction of the properties of molecules and materials. First-order GNNs are well known to be incomplete, i.e. there exist graphs that ...

IOP Publishing Ltd2022

Comment on "Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions" [J. Chem. Phys. 156, 034302 (2022)]

Michele Ceriotti, Michael John Willatt, Sergey Pozdnyakov

The "quasi-constant " smooth overlap of atomic position and atom-centered symmetry function fingerprint manifolds recently discovered by Parsaeifard and Goedecker [J. Chem. Phys. 156, 034302 (2022)] are closely related to the degenerate pairs of configurat ...

AIP Publishing2022

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

Local invertibility and sensitivity of atomic structure-feature mappings

Michele Ceriotti, Sergey Pozdnyakov

Background: The increasingly common applications of machine-learning schemes to atomic-scale simulations have triggered efforts to better understand the mathematical properties of the mapping between the Cartesian coordinates of the atoms and the variety o ...

2021

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

Recursive evaluation and iterative contraction of N-body equivariant features

Michele Ceriotti, Jigyasa Nigam, Sergey Pozdnyakov

Mapping an atomistic configuration to a symmetrized N-point correlation of a field associated with the atomic positions (e.g., an atomic density) has emerged as an elegant and effective solution to represent structures as the input of machine-learning algo ...

2020

Incompleteness of Atomic Structure Representations

Michele Ceriotti, Michael John Willatt, Sergey Pozdnyakov

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