MALOQ introduces a scalable SO(2)-equivariant ML framework with custom kernels and edge-wise graph distribution for predicting large-scale quantum transport operators.
Physics-informed hamiltonian learning for large-scale optoelectronic property prediction,
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MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
MALOQ introduces a scalable SO(2)-equivariant ML framework with custom kernels and edge-wise graph distribution for predicting large-scale quantum transport operators.