Optimize neighbor list and k-point eigensolver - #6
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Replace O(N_cells·N²) dense distance allocation with matscipy cell-list neighbor detection (O(N·z̄)), then rebuild only the relevant cells differentiably from positions so autograd is preserved for forces/stress. Batch all k-points into a single eighb call instead of a Python for-loop, removing serial overhead and letting cuBLAS/LAPACK parallelize across k-points.
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N = number of atoms being considered
z = average number of neighbors for each atom being considered
K = number of k-points
Replace O(N_cells·N²) dense distance allocation with matscipy cell-list neighbor detection (O(N·z)), then rebuild only the relevant cells differentiably from positions so autograd is preserved for forces/stress.
Batch all k-points into a single eighb call instead of a Python for-loop, removing serial overhead and letting cuBLAS/LAPACK parallelize across k-points. Still O(K*N^3), but now it can run in parallel on GPU
Used Claude for this, may be a work in progress