  Property prediction accuracy has long been a key parameter of machine
learning in materials informatics. Accordingly, advanced models showing
state-of-the-art performance turn into highly parameterized black boxes missing
interpretability. Here, we present an elegant way to make their reasoning
transparent. Human-readable text-based descriptions automatically generated
within a suite of open-source tools are proposed as materials representation.
Transformer language models pretrained on 2 million peer-reviewed articles take
as input well-known terms, e.g., chemical composition, crystal symmetry, and
site geometry. Our approach outperforms crystal graph networks by classifying
four out of five analyzed properties if one considers all available reference
data. Moreover, fine-tuned text-based models show high accuracy in the
ultra-small data limit. Explanations of their internal machinery are produced
using local interpretability techniques and are faithful and consistent with
domain expert rationales. This language-centric framework makes accurate
property predictions accessible to people without artificial-intelligence
expertise.
