Polarizable embedding with a multiconfiguration short-range density functional theory linear response method

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Weakly nonlinear evolution of stochastically driven non-normal systems

François Gallaire, Edouard Boujo, Yves-Marie François Ducimetière

We consider nonlinear dynamical systems driven by stochastic forcing. It has been largely evidenced in the literature that the linear response of non-normal systems (e.g. fluid flows) may exhibit a large variance amplification, even in a linearly stable re ...

Cambridge University Press2022

Mesh d-refinement: a data-based computational framework to account for complex material response

Jean-François Molinari, Sacha Zenon Wattel, Antonio Joaquin Garcia Suarez

Model-free data-driven computational mechanics (DDCM) is a new paradigm for simulations in solid mechanics. The modeling step associated to the definition of a material constitutive law is circumvented through the introduction of an abstract phase space in ...

2023

Frequency-domain data-driven position-dependent controller synthesis for Cartesian Robots

Alireza Karimi, Philippe Louis Schuchert

Cartesian robots have position-dependent dynamics that should be accounted for in high performance applications. Traditional methods design Linear Time Invariant (LTI) controllers which are robustly stable with respect to position variations, but require a ...

2023

Anisotropic and nonlinear transport in microstructured 3D topological semimetals

Xiangwei Huang

Topological semimetals are frequently discussed as materials platforms for future electronics that exploit the remarkable properties of their quasiparticles. These ideas are mostly based on dispersion relations that mimic relativistic particles, such as We ...

EPFL2022

Ultra low quantum decoherence nano-optomechanical systems

Mohammadjafar Bereyhi

Thermal motion of a room-temperature mechanical resonator typically dominates the quantum backaction of its position measurement. This is a longstanding barrier for exploring cavity optomechanics at room temperature. In order to enter the quantum regime of ...

EPFL2022

Assessing the persistence of chalcogen bonds in solution with neural network potentials

Anne-Clémence Corminboeuf, Veronika Juraskova, Rubén Laplaza Solanas, Frédéric Célerse

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environme ...

2022

Optoelectronics based on the spin-valley physics of 2D materials

Juan Francisco Gonzalez Marin

Two-dimensional (2D) materials are atomically thin crystals with exceptional mechanical, electrical and optical properties. Their unique characteristics originating from quantum confinement in the vertical dimension have attracted a strong interest for sci ...

EPFL2022

From coating flow patterns to porous body wake dynamics via multiscale models

Pier Giuseppe Ledda

Multiscale phenomena are involved in countless problems in fluid mechanics. Coating flows are known to exhibit a broad variety of patterns, such as wine tears in a glass and dripping of fresh paint applied on a wall. Coating flows are typically modeled und ...

EPFL2022

Self-consistent Hubbard parameters from density-functional perturbation theory in the ultrasoft and projector-augmented wave formulations

Nicola Marzari, Matteo Cococcioni, Iurii Timrov

The self-consistent evaluation of Hubbard parameters using linear-response theory is crucial for quantitatively predictive calculations based on Hubbard-corrected density-functional theory. Here, we extend a recently introduced approach based on density-fu ...

AMER PHYSICAL SOC2021

Predictors and predictands of linear response in spatially extended systems

Umberto Maria Tomasini

The goal of response theory, in each of its many statistical mechanical formulations, is to predict the perturbed response of a system from the knowledge of the unperturbed state and of the applied perturbation. A new recent angle on the problem focuses on ...

SPRINGER HEIDELBERG2021

Networks of Coupled VO2 Oscillators for Neuromorphic Computing

Elisabetta Corti

Neuromorphic computing is a wide research field aimed to the realization of brain-inspired hardware, apt to tackle computation of unstructured data more efficiently than currently done with standard computational units. Oscillatory neural networks are know ...

EPFL2021

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