Data-efficient machine learning mimicking human intelligence in fundamental materials science

Machine learning (ML) can be used to solve previously unsolvable materials science problems like highly nonlinear structure-property relationship and high dimensional multifactorial designs. However, requirement for data quality and quantity are not easily satisfied for general ML algorithms. As a result, usually significant experimental resources are needed in generating required data sets. On the other hand, it is widely accepted that human intelligence has a significant advantage in terms of data-efficiency. Experienced materials scientists can usually provide directional insights based on only a few data points. Thus combining the power of machine intelligence (i.e. handling highly nonlinear systems and high dimensional information) and human intelligence (i.e. creating context and making comparison/analogy in materials science language) can enable high data efficiency and reduce development cost for new materials. In this talk, I will give an introduction on data-efficient machine learning that mimics human intelligence in terms of creating digital counterpart of fundamental materials science concepts. These concepts include product by process, materials by construction, graphical representation based pattern recognition, reverse engineering through latent space learning, design by human sensory, fuzzy logic in computational trial and error.