Computational study demonstrates an event-sourced architecture on large experimental materials databases, highlighting accelerated discovery through provenance-based machine learning.
We present a generalizable database architecture ESAMP that captures the complete provenance associated with a material. We demonstrate this architecture and provenance based machine learning on one of the largest experimental materials databases.
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Statt et al. (2023) studied this question.
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