Scalable machine learning models for predicting quantum transport in disordered 2D hexagonal materials

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For well-distributed points, nearest neighbor search is often near O(log⁡n)O(\log n)O(logn) in practice. In the worst case (all points clustered tightly or along a line), it can degrade to O(n)O(n)O(n), but this is uncommon with typical spatial data.

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