Unveil the unseen: Exploit information hidden in noise

However, discovering new phenomena is not the only challenge: utilizing the freshly obtained knowledge for real-world applications is crucial. With the availability of computers and large amounts of experimental/- computational data nowadays [1-4], machine learning [5-9] has proven an effective tool for this purpose. Machine learning is a class of methods that start from existing data to train a model and then predict the quantities of interest useful for a given application. For example, machine learning can predict many properties of a putative material [10-18], and moreover can understand the uncertainty in those predictions. This uncertainty can be used to design the material that is most likely to satisfy the set target criteria [19-21], avoiding the typical expensive and time-consuming cycles of trial and improvement experiments. Furthermore, the uncertainty is useful for accelerating materials discovery by guiding where new experiments should be performed in the materials space [22-24], and also for the identification of outliers and erroneous entries in materials databases [25]. While uncertainty is crucial for focusing on the most viable candidates for a given application, uncertainty itself could be a useful value for property prediction. This strategy is motivated by Wilson's Renormalization Group theory [26], in which fluctuations on all scales determine the macroscopic state of the system.