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ESAMP: Event-Sourced Architecture for Materials Provenance Management and Application to Accelerated Materials Discovery
[post]
2021
unpublished
While the vision of accelerating materials discovery using data driven methods is well-founded, practical realization has been throttled due to challenges in data generation, ingestion, and materials state-aware machine learning. High-throughput experiments and automated computational workflows are addressing the challenge of data generation, and capitalizing on these emerging data resources requires ingestion of data into an architecture that captures the complex provenance of experiments and
doi:10.26434/chemrxiv.14583258.v1
fatcat:uh2sur57bjbxfom6s25kqq53ym