35 citations · 35 across the 4 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…
Mechanisms of Resistive Switching in 2D Monolayer and Multilayer Materials
M. Kaniselvan, Y. R. Jeon, M. Mladenović +2
The power and energy consumption of resistive switching devices can be lowered by reducing their active layer dimensions. Efforts to push this low-energy switching property to its…
Learning from the electronic structure of molecules across the periodic table
Manasa Kaniselvan, Benjamin Kurt Miller, Meng Gao +2
Machine-Learned Interatomic Potentials (MLIPs) require vast amounts of atomic structure data to learn forces and energies, and their performance continues to improve with training…
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…
Termination-Dependent Resistive Switching in SrTiO Valence Change Memory Cells
Marko Mladenović, Manasa Kaniselvan, Christoph Weilenmann +2
Valence change memory (VCM) cells based on SrTiO (STO), a perovskite oxide, are a promising type of emerging memory device. While the operational principle of most VCM cells re…