  Machine learning with application to questions in the physical sciences has
become a widely used tool, successfully applied to classification, regression
and optimization tasks in many areas. Research focus mostly lies in improving
the accuracy of the machine learning models in numerical predictions, while
scientific understanding is still almost exclusively generated by human
researchers analysing numerical results and drawing conclusions. In this work,
we shift the focus on the insights and the knowledge obtained by the machine
learning models themselves. In particular, we study how it can be extracted and
used to inspire human scientists to increase their intuitions and understanding
of natural systems. We apply gradient boosting in decision trees to extract
human interpretable insights from big data sets from chemistry and physics. In
chemistry, we not only rediscover widely know rules of thumb but also find new
interesting motifs that tell us how to control solubility and energy levels of
organic molecules. At the same time, in quantum physics, we gain new
understanding on experiments for quantum entanglement. The ability to go beyond
numerics and to enter the realm of scientific insight and hypothesis generation
opens the door to use machine learning to accelerate the discovery of
conceptual understanding in some of the most challenging domains of science.
