  In condensed matter physics and materials science, predicting material
properties necessitates understanding intricate many-body interactions.
Conventional methods such as density functional theory (DFT) and molecular
dynamics (MD) often resort to simplifying approximations and are
computationally expensive. Meanwhile, recent machine learning methods use
handcrafted descriptors for material representation which sometimes neglect
vital crystallographic information and are often limited to single property
prediction or a sub-class of crystal structures. In this study, we pioneer an
unsupervised strategy, drawing inspiration from Natural Language Processing
(NLP), to harness the underutilized potential of Crystallographic Information
Files (CIFs). We conceptualize atoms and atomic positions within a CIF
similarly to words in textual content. Using a Word2Vec-inspired technique, we
produce atomic embeddings that capture intricate atomic relationships. Our
model, CIFSemantics, trained on the extensive Material Project dataset, adeptly
predicts 15 distinct material properties from the CIFs. Its performance rivals
specialized models, marking a significant step forward in material property
predictions.
