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cs.LG2026
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…
cs.LG2025
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…