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20062026
most citedAn Efficient and Accurate Car-Parrinello-like Approach to Born-Oppenheimer Molecular Dynamics

463 citations · 2.3k across the 58 of their papers we have counts for

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11 papers · 1 filter

physics.comp-ph2026

From Bellissard's gap labeling to atomistic quasiperiodic bilayers

Thomas D. Kühne, Vladislav Efremkin, Emil Prodan

Bellissard's framework uses covariant observables to describe aperiodic solids. We examine how butterfly-like spectra in twisted bilayers can be related to bulk gap labels and topo…

physics.comp-ph2022★ 2 cited

Breaking the Exascale Barrier for the Electronic Structure Problem in Ab-Initio Molecular Dynamics

Robert Schade, Tobias Kenter, Hossam Elgabarty +3

The non-orthogonal local submatrix method applied to electronic-structure based molecular dynamics simulations is shown to exceed 1.1 EFLOP/s in FP16/FP32 mixed floating-point arit…

physics.comp-ph2021★ 60 cited

Towards Electronic Structure-Based Ab-Initio Molecular Dynamics Simulations with Hundreds of Millions of Atoms

Robert Schade, Tobias Kenter, Hossam Elgabarty +8

We push the boundaries of electronic structure-based \textit{ab-initio} molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise barely reachable with classical…

physics.comp-ph2020★ 12 cited

Disordered Crystals from First Principles II: Transport Coefficients

Thomas D. Kühne, Julian Heske, Emil Prodan

This is the second part of a project on the foundations of first-principle calculations of the electron transport in crystals at finite temperatures, aiming at a predictive first-p…

physics.comp-ph2019★ 8 cited

Accurate Sampling with Noisy Forces from Approximate Computing

Varadarajan Rengaraj, Michael Lass, Christian Plessl +1

In scientific computing, the acceleration of atomistic computer simulations by means of custom hardware is finding ever growing application. A major limitation, however, is that th…

physics.comp-ph2019★ 24 cited

Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo

Jan Kessler, Francesco Calcavecchia, Thomas D. Kühne

Inspired by the universal approximation theorem and widespread adoption of artificial neural network techniques in a diversity of fields, we propose feed-forward neural networks as…