MALOQ introduces a scalable SO(2)-equivariant ML framework with custom kernels and edge-wise graph distribution for predicting large-scale quantum transport operators.
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions,
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Bipartite Cholesky Graph Networks from density-fitted ERI decomposition achieve 0.0296 Ha in-distribution MAE on six diatomic molecules under FCI reference, outperforming compressed-integral baselines, with generalization tied to orbital environment similarity.
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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.
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Bipartite Cholesky Graph Networks for Many-Body Quantum Chemistry
Bipartite Cholesky Graph Networks from density-fitted ERI decomposition achieve 0.0296 Ha in-distribution MAE on six diatomic molecules under FCI reference, outperforming compressed-integral baselines, with generalization tied to orbital environment similarity.