Issue 5, 2023

Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements

Abstract

High-throughput experimentation enables efficient search space exploration for the discovery and optimization of new materials. However, large search spaces, e.g. of compositionally complex materials, require decreasing characterization times significantly. Here, an autonomous measurement algorithm was developed, which leverages active learning with a Gaussian process model capable of iteratively scanning a materials library based on the highest uncertainty. The algorithm is applied to a four-point probe electrical resistance measurement device, frequently used to obtain indications for regions of interest in materials libraries. Ten libraries with different complexities of composition and property trends are analyzed to validate the model. By stopping the process before the entire library is characterized and predicting the remaining areas, the measurement efficiency can be improved drastically. As robustness is essential for autonomous measurements, intrinsic outlier handling is built into the model and a dynamic stopping criterion based on the mean predicted covariance is proposed. A measurement time reduction of about 70–90% was observed while still ensuring an accuracy above 90%.

Graphical abstract: Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements

Supplementary files

Article information

Article type
Paper
Submitted
05 Jul 2023
Accepted
19 Sep 2023
First published
19 Sep 2023
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2023,2, 1612-1619

Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements

F. Thelen, L. Banko, R. Zehl, S. Baha and A. Ludwig, Digital Discovery, 2023, 2, 1612 DOI: 10.1039/D3DD00125C

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements