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.
We aim to develop active learning approaches. The challenge that we face is the high dimension of the material
- Active Learning allows us to reduce the number of experiments that we spend in order to find optimal
- Active Learning helps us to automatize the experimental process.