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

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This fragmentation hurts portability. Code that performs well on one runtime may behave differently (or poorly) on another, even though it's using "standard" APIs. The complexity burden on runtime implementers is substantial, and the subtle behavioral differences create friction for developers trying to write cross-runtime code, particularly those maintaining frameworks that must be able to run efficiently across many runtime environments.

(and thanks to Matthew Miller for reviewing and providing feedback on this post)

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