Optimal radial basis for density-based atomic representations

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Magnetic orderings and electronic structure of complex oxides from advanced Hubbard functionals

Luca Binci

Transition metal oxides represent a class of materials displaying very unusual electronic, structural and magnetic properties. They are extremely interesting, both from a technological and fundamental point of view. The most important characteristic of the ...

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Structural topology exploration through policy-based generation of equilibrium representations

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Mainstream approaches to design spatial architectural forms that are structurally relevant consist either in adapting well-known and catalogued conventional types or in searching for close-to-optimum solutions of well-defined problems. Few means exist to e ...

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Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

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Objective. Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon ...

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Universal machine learning for the response of atomistic systems to external fields

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Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...

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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 ...

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

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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 ...

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High-order geometric integrators for nonadiabatic molecular quantum dynamics and their applications to explore conical intersections

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Accurate simulations of molecular quantum dynamics are crucial for understanding numerous natural processes and experimental results. Yet, such high-accuracy simulations are challenging even for relatively simple systems where the Born-Oppenheimer approxim ...

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Reduced representations of complexes, signals, and multifiltrations

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The field of computational topology has developed many powerful tools to describe the shape of data, offering an alternative point of view from classical statistics. This results in a variety of complex structures that are not always directly amenable for ...

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Magnetic orderings and electronic structure of complex oxides from advanced Hubbard functionals

Luca Binci

EPFL2023

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Silvestro Micera, Simone Romeni, Elena Losanno, Luca Pierantoni

IOP Publishing Ltd2023

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

Michele Ceriotti, Guillaume André Jean Fraux, Sergey Pozdnyakov, Jigyasa Nigam

AIP Publishing2022

High-order geometric integrators for nonadiabatic molecular quantum dynamics and their applications to explore conical intersections

Seonghoon Choi

EPFL2022