Experimental data management platform for data-driven investigation of combinatorial alloy thin films

TL;DR

The paper presents TFADB, a flexible data management platform for combinatorial alloy thin film experiments, enabling efficient data handling and machine learning analysis of heterogeneous experimental data.

Contribution

Introduction of TFADB, a novel, adaptable database platform tailored for managing complex, multidimensional alloy thin film experimental data.

Findings

Facilitates easy upload, editing, and retrieval of diverse experimental data.

Supports composition-dependent property management for machine learning.

Flexible architecture allows integration of new data types from future experiments.

Abstract

Experimental materials data are heterogeneous and include a variety of metadata for processing and characterization conditions, making the implementation of data-driven approaches for developing novel materials difficult. In this paper, we introduce the Thin-Film Alloy Database (TFADB), a materials data management platform, designed for combinatorially investigated thin-film alloys through various experimental tools. Using TFADB, researchers can readily upload, edit, and retrieve multidimensional experimental alloy data, such as composition, thickness, X-ray Diffraction, electrical resistivity, nanoindentation, and image data. Furthermore, composition-dependent properties from the database can easily be managed in a format adequate to be preprocessed for machine learning analyses. High flexibility of the software allows management of new types of materials data that can be potentially acquired from new combinatorial experiments.