Mathieu Luisier

Experimentally constrained modeling of the Pockels response of KNbO3 and KTaNbO3

Virginie de Mestral, Lorenzo Bastonero, Petr Bednyakov, Fedir Borodavka et autres

The soft-mode of electro-optic (EO) metal-oxide perovskites plays a critical role in determining their Pockels responses. This is the case of potassium tantalate niobate (KTN), which exhibits an intrinsic Pockels response 2.5 times larger than that of state-of-the-art barium titanate (BTO), highlighting …

ch, us, cz, be (code pays fourni par la source)

Experimentally constrained modeling of the Pockels response of KNbO3 and KTaNbO3

Virginie de Mestral, Lorenzo Bastonero, Petr Bednyakov, Fedir Borodavka et autres

The soft-mode of electro-optic (EO) metal-oxide perovskites plays a critical role in determining their Pockels response. This is the case of potassium tantalate niobate (KTN), which exhibits an intrinsic Pockels response 2.5 times larger than that of state-of-the-art barium titanate (BTO), highlighting …

ch, us, cz, be (code pays fourni par la source)

Parallel Quadratic Selected Inversion in Quantum Transport Simulation

Vincent Maillou, Matthias Bollhöfer, Olaf Schenk, Alexandros Nikolaos Ziogas et autres

Driven by Moore’s law, the dimensions of transistors have been pushed down to the nanometer scale so that advanced quantum transport (QT) solvers are nowadays required to reliably design such nano-devices. The non-equilibrium Green’s function (NEGF) formalism is suited to this task …

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EmuGEMM: Fused Tensor Core Kernels for Precision Emulation in Matrix Multiplication

Denghui Lu, Alexander Maeder, Mathieu Luisier, Alexandros Nikolaos Ziogas

Modern GPUs devote an increasing silicon budget to low-precision matrix-multiplication units, widening the precision-throughput gap for scientific computing workloads. Ozaki Schemes I and II offer an alternative by reconstructing high-precision general matrix multiplication (GEMM) from low-precision operations, yet existing implementations leave substantial …

ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations

Afif Boudaoud, Lisa Gaedke-Merzhäuser, Alexandros Nikolaos Ziogas, Vincent Maillou et autres

Spatio-temporal Bayesian inference drives environmental and health sciences using latent Gaussian models. Integrated Nested Laplace Approximations (INLA) enable inference for these models at HPC scale but rely on derivative-based optimization over $d$ hyperparameters. State-of-the-art INLA implementations approximate derivatives via central finite differences …

ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations

Afif Boudaoud, Lisa Gaedke-Merzhäuser, Alexandros Nikolaos Ziogas, Vincent Maillou et autres

Spatio-temporal Bayesian inference drives environmental and health sciences using latent Gaussian models. Integrated Nested Laplace Approximations (INLA) enable inference for these models at HPC scale but rely on derivative-based optimization over $d$ hyperparameters. State-of-the-art INLA implementations approximate derivatives via central finite differences …

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Strongly correlated physics in organic open-shell quantum systems

Guido Gandus, Anooja Jayaraj, Daniele Passerone, Robert Stadler et autres

Strongly correlated physics arises from electron-electron scattering within partially filled orbitals. Organic molecules in open-shell configurations are therefore good candidates to exhibit many-body effects. We focus on electron transport in a two-terminal single-molecule junction setup, in which the molecular bridge consists of …

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Atomistic Origin of RTN-like Centers Created and Annihilated by RRAM Write Processes

P. M. Solomon, Manasa Kaniselvan, Hiroyuki Miyazoe, Babar Khan et autres

We examine in detail random-telegraph-like noise (RTN) generated in resistive random-access memory (ReRAM) structures as a consequence of applying programming pulses. We find that a single programming pulse can induce highly correlated RTN noise patterns, in terms of frequency and amplitude, which …

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Actor–critic networks with analogue memristors mimicking reward-based learning

Kevin Portner, Till Zellweger, Flavio Martinelli, Laura Bégon‐Lours et autres

Advancements in memristive devices have given rise to a new generation of specialized hardware for bio-inspired computing. However, most of these implementations draw only partial inspiration from the architecture and functionalities of the mammalian brain. Moreover, the use of memristive hardware is …

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Oxide induced degradation in MoS2 Field-Effect Transistors

Fabian Ducry, Benoît Van Troeye, Mauro Dossena, Mathieu Luisier et autres

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Accelerated Spatio-Temporal Bayesian Modeling for Multivariate Gaussian Processes

Lisa Gaedke-Merzhäuser, Vincent Maillou, Fernando Rodriguez Avellaneda, Olaf Schenk et autres

Multivariate Gaussian processes (GPs) offer a powerful probabilistic framework to represent complex interdependent phenomena. They pose, however, significant computational challenges in high-dimensional settings, which frequently arise in spatio-temporal applications. We present DALIA, a highly scalable framework for performing Bayesian inference tasks on …

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Ab-initio Quantum Transport with the GW Approximation, 42,240 Atoms, and Sustained Exascale Performance

Nicolas Vetsch, Alexander Maeder, Vincent Maillou, Anders Winka et autres

Designing nanoscale electronic devices such as the currently manufactured nanoribbon field-effect transistors (NRFETs) requires advanced modeling tools capturing all relevant quantum mechanical effects. State-of-the-art approaches combine the non-equilibrium Green’s function (NEGF) formalism and density functional theory (DFT). However, as device dimensions do …

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