Motivation

In materials science, the generation of data sets that are suitable for machine learning (ML) applications is usually expensive. Generally, the data space is complex and requires many samples to be represented. For this purpose, not only the number of samples matters, but also the information content of each sample. Active learning methods are used for simultaneous learning and data acquisition simultaneously learn relations and acquire the data points. Active learning algorithms know what they do not know and identify the data points that have the highest potential for increasing the knowledge.

Objectives

We aim to develop active learning approaches. The challenge that we face is the high dimension of the material space.

• Active Learning allows us to reduce the number of experiments that we spend in order to find optimal settings.
• Active Learning helps us to automatize the experimental process.