ESAMP: Event-Sourced Architecture for Materials Provenance Management and Application to Accelerated Materials Discovery

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 simulations. In this manuscript, we describe an event-sourced architecture for materials provenance (ESAMP) that encodes the sequence and interrelationships among events occurring in a simulation or experiment. We use this architecture to ingest a large and varied dataset (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments performed using various modalities such as serial, parallel, multimodal experimentation. Our data architecture tracks the evolution of a material’s state, enabling a demonstration of how stateequivalency rules can be used to generate datasets that significantly enhance data-driven materials discovery. Specifically, using state-equivalency rules and parameters associated with statechanging processes in addition to the typically used composition data, we demonstrated marked reduction of uncertainty in prediction of overpotential for oxygen evolution reaction (OER) catalysts. Finally, we discuss the importance of ESAMP architecture in enabling several aspects of accelerated materials discovery such as dynamic workflow design, generation of knowledge graphs, and efficient integration of theory and experiment.

ESAMP: Event-Sourced Architecture for Materials Provenance Management and Application to Accelerated Materials Discovery

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 simulations. In this manuscript, we describe an event-sourced architecture for materials provenance (ESAMP) that encodes the sequence and interrelationships among events occurring in a simulation or experiment. We use this architecture to ingest a large and varied dataset (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments performed using various modalities such as serial, parallel, multimodal experimentation. Our data architecture tracks the evolution of a material’s state, enabling a demonstration of how stateequivalency rules can be used to generate datasets that significantly enhance data-driven materials discovery. Specifically, using state-equivalency rules and parameters associated with statechanging processes in addition to the typically used composition data, we demonstrated marked reduction of uncertainty in prediction of overpotential for oxygen evolution reaction (OER) catalysts. Finally, we discuss the importance of ESAMP architecture in enabling several aspects of accelerated materials discovery such as dynamic workflow design, generation of knowledge graphs, and efficient integration of theory and experiment.