Fig. 1: Impact of datasets and feature sets in implementing ML for materials research.
From: Why big data and compute are not necessarily the path to big materials science
a Materials literature with a heterogeneous dataset due to domain bias and selection bias. Domain bias results when training datasets do not adequately cover the research space. Selection bias arises when some external factors such as questionability and inexplicability restrict the likelihood of a data inclusion in the datasets; such data can be either experimental, theoretical, or computational. b Holistic description of the synthesis, composition, microstructure, and macrostructure of materials, which are related to material properties and performance. Identifying a sufficient feature space with essential variables such as synthesis parameters requires careful observation and lateral thinking.
