Unravelling the interplay between hydrogen and grain boundary of α-Fe under different concentration and strain rate via neural network interatomic potential

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Instead of tee() with its hidden unbounded buffer, you get explicit multi-consumer primitives. Stream.share() is pull-based: consumers pull from a shared source, and you configure the buffer limits and backpressure policy upfront.

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