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In condensed matter physics and materials science, predicting materialproperties necessitates understanding intricate many-body interactions.Conventional methods such as density functional theory (DFT) and moleculardynamics (MD) often resort to simplifying approximations and arecomputationally expensive. Meanwhile, recent machine learning methods usehandcrafted descriptors for material representation which sometimes neglectvital crystallographic information and are often limited to single propertyprediction or a sub-class of crystal structures. In this study, we pioneer anunsupervised strategy, drawing inspiration from Natural Language Processing(NLP), to harness the underutilized potential of Crystallographic InformationFiles (CIFs). We conceptualize atoms and atomic positions within a CIFsimilarly to words in textual content. Using a Word2Vec-inspired technique, weproduce atomic embeddings that capture intricate atomic relationships. Ourmodel, CIFSemantics, trained on the extensive Material Project dataset, adeptlypredicts 15 distinct material properties from the CIFs. Its performance rivalsspecialized models, marking a significant step forward in material propertypredictions.

Atoms as Words: A Novel Approach to Deciphering Material Properties using NLP-inspired Machine Learning on Crystallographic Information Files (CIFs)