HTEM-DB

HTEM-DB stores inorganic thin-film materials data from combinatorial experiments at the National Renewable Energy Laboratory (NREL) to provide structured datasets for materials science research and machine learning.

Key Features:

• Data repository: Stores and manages experimental data and associated metadata for inorganic thin-film combinatorial experiments.
• Research Data Infrastructure (RDI) integration: Integrates with NREL's RDI via custom-developed tools for data collection, processing, and storage.
• Instrument integration and pipeline: Connects data tools with experimental instruments to establish a pipeline that transfers experimental outputs into the repository and links experimental researchers with data scientists.
• Structured datasets for machine learning: Aggregates materials data in a structured manner to enhance utility for machine learning studies.
• High-throughput combinatorial focus: Captures outputs from high-throughput combinatorial thin-film experiments.
• Data quality and comprehensiveness: Systematically captures comprehensive, high-quality datasets for subsequent analysis.

Scientific Applications:

• Machine learning datasets: Provides structured, annotated datasets for machine learning studies in materials science.
• Accelerated materials discovery: Supports data-driven acceleration of discovery and innovation in inorganic thin-film materials.
• Bridging experiment and data science: Facilitates collaboration between experimental researchers and data scientists through an integrated data pipeline.
• Institutional workflow model: Serves as a model for other institutions developing similar data-centric experimental workflows.

Methodology:

Custom-developed tools within NREL's Research Data Infrastructure collect, process, and store experimental data and associated metadata from combinatorial inorganic thin-film experiments and integrate with experimental instruments to deliver datasets into HTEM-DB.