Active learning for predicting the enthalpy of mixing in binary liquids based on ab initio molecular dynamics

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The gains illustrate how fundamental design choices compound: batching amortizes async overhead, pull semantics eliminate intermediate buffering, and the freedom for implementations to use synchronous fast paths when data is available immediately all contribute.

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