2 citations · 2 across the 5 of their papers we have counts for
8 papers
Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
Mathieu Luisier, Nicolas Vetsch, Alexander Maeder +8
The Non-equilibrium Green's function (NEGF) formalism is a particularly powerful method to simulate the quantum transport properties of nanoscale devices such as transistors, photo…
Machine-Learned Hamiltonians for Quantum Transport Simulation of Valence Change Memories
Chen Hao Xia, Manasa Kaniselvan, Marko Mladenoivić +1
The construction of the Hamiltonian matrix \textbf{H} is an essential, yet computationally expensive step in \textit{ab-initio} device simulations based on density-functional theor…
Parallel Quadratic Selected Inversion in Quantum Transport Simulation
Vincent Maillou, Matthias Bollhofer, Olaf Schenk +2
Driven by Moore's Law, the dimensions of transistors have been pushed down to the nanometer scale. Advanced quantum transport (QT) solvers are required to accurately simulate such…
Accelerated Spatio-Temporal Bayesian Modeling for Multivariate Gaussian Processes
Lisa Gaedke-Merzhäuser, Vincent Maillou, Fernando Rodriguez Avellaneda +5
Multivariate Gaussian processes (GPs) offer a powerful probabilistic framework to represent complex interdependent phenomena. They pose, however, significant computational challeng…
Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction
Manasa Kaniselvan, Alexander Maeder, Chen Hao Xia +2
Equivariant Graph Neural Networks (eGNNs) trained on density-functional theory (DFT) data can potentially perform electronic structure prediction at unprecedented scales, enabling…
Electroforming Kinetics in HfOx/Ti RRAM: Mechanisms Behind Compositional and Thermal Engineering
Manasa Kaniselvan, Kevin Portner, Donato Francesco Falcone +6
A critical issue affecting filamentary resistive random access memory (RRAM) cells is the requirement of high voltages during electroforming. Reducing the magnitude of these voltag…