35 citations · 50 across the 15 of their papers we have counts for
4 papers · 1 filter
Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures
Manasa Kaniselvan, Mauro Dossena, Denghui Lu +4
We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with an advanced quantum transport…
MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
Manasa Kaniselvan, Alexander Maeder, Denghui Lu +2
Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extend…
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…