2 citations · 2 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
Serinv: A Scalable Library for the Selected Inversion of Block-Tridiagonal with Arrowhead Matrices
Vincent Maillou, Lisa Gaedke-Merzhaeuser, Alexandros Nikolaos Ziogas +2
The inversion of structured sparse matrices is a key but computationally and memory-intensive operation in many scientific applications. There are cases, however, where only partic…
Learning the Electronic Hamiltonian of Large Atomic Structures
Chen Hao Xia, Manasa Kaniselvan, Alexandros Nikolaos Ziogas +4
Graph neural networks (GNNs) have shown promise in learning the ground-state electronic properties of materials, subverting ab initio density functional theory (DFT) calculations w…