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Related Experiment Video

Updated: Oct 8, 2025

The Automated Crystallography Pipelines at the EMBL HTX Facility in Grenoble

The Automated Crystallography Pipelines at the EMBL HTX Facility in Grenoble

Published on: June 5, 2021

Research data infrastructure for high-throughput experimental materials science.

Kevin R Talley¹, Robert White¹, Nick Wunder¹

¹Materials, Chemical and Computational Science Directorate, National Renewable Energy Laboratory, Golden, CO 80401, USA.

Patterns (New York, N.Y.)

|December 24, 2021

Summary

This summary is machine-generated.

The High-Throughput Experimental Materials Database (HTEM-DB) now centralizes inorganic thin-film data from combinatorial experiments. This facilitates materials discovery and design through improved data accessibility and machine learning applications.

Keywords:

data experimental high-throughput materials metadata workflow

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Last Updated: Oct 8, 2025

The Automated Crystallography Pipelines at the EMBL HTX Facility in Grenoble

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Area of Science:

Materials Science
Data Science
Renewable Energy

Background:

The National Renewable Energy Laboratory (NREL) collects extensive inorganic thin-film materials data.
Existing data is often siloed, limiting its use for advanced analysis.
A need exists for integrated data repositories to accelerate materials research.

Purpose of the Study:

To describe the data flow from experimental instruments to the High-Throughput Experimental Materials Database (HTEM-DB).
To illustrate NREL's strategies and best practices for managing materials data.
To encourage the adoption of similar data workflows at other institutions.

Main Methods:

Utilizing NREL's Research Data Infrastructure (RDI) for data collection, processing, and storage.
Integrating custom data tools with experimental instruments to create a data pipeline.
Establishing metadata standards for experimental data.

Main Results:

Successful implementation of a data flow from RDI to HTEM-DB.
Demonstration of a functional data communication pipeline between researchers and data scientists.
Creation of a valuable data asset for materials science research.

Conclusions:

The described data workflow enhances the accessibility and utility of materials data.
Aggregated materials data can significantly accelerate machine learning-driven discovery and design.
Adoption of these practices can foster collaboration and advance the field of materials science.