An open experimental database for exploring inorganic materials.

Area of Science:

• Materials Science
• Data Science
• Computational Materials Science

Background:

• Advanced machine learning in materials science is hindered by limited, non-diverse datasets.
• Publicly accessible data resources are crucial for materials research, especially for scientists without access to specialized equipment.

Purpose of the Study:

• To report on the development and progress of a publicly open High Throughput Experimental Materials (HTEM) Database.
• To demonstrate the utility of the HTEM database for materials exploration and machine learning applications.

Main Methods:

• Development of a High Throughput Experimental (HTE) approach for generating materials data.
• Implementation of a Laboratory Information Management System (LIMS) to underpin the database.
• Curating and organizing over 140,000 sample entries with diverse material properties.

Main Results:

• The HTEM Database contains 140,000 sample entries, including structural, synthetic, chemical, and optoelectronic properties of inorganic thin films.
• Over 4,000 sample entries span more than 100 materials systems, with more than half of the data publicly available.
• The database offers both a web-based user interface and an application programming interface (API) for data access.

Conclusions:

• The HTEM Database provides a valuable, open-access resource for the materials science community.
• This database enables broader adoption of machine learning algorithms for materials discovery and research.
• The HTEM initiative promotes data sharing and accessibility, democratizing materials research.