Combining Machine Learning and Formal Techniques for Small Data Applications - A Framework to Explore New Structural Materials

Abstract

The massive increase in computation power leads to a renaissance of supervised learning techniques, which were published decades ago but have so far been confined to theory. These techniques form the increasingly important field of Machine Learning (ML), which contributes to a large variety of research concerning industrial, automotive but also consumer applications strongly influencing our daily life. Commonly, the learning techniques require a set of labeled data, which involves a resource-intensive generation, to conduct the training. Depending on the dimensionality of the data and the required precision as needed by the application, the amount of training data varies. In case of insufficient training data, the prediction is of low-quality or not even possible at all, restricting the applicability of ML. This work proposes a combination of formal techniques and ML to implement a framework that allows coping with high-dimensional, training data while retaining a high prediction quality. The efficacy of this method is exemplarily demonstrated on the basis of an interdisciplinary material science research problem concerning the development of new structural materials, though it can be adapted to further applications.