LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline
  Solids From Their Text Descriptions
Andre Niyongabo Rubungo Craig Arnold Barry P. Rand Adji Bousso Dieng
The prediction of crystal properties plays a crucial role in the crystal
design process. Current methods for predicting crystal properties focus on
modeling crystal structures using graph neural networks (GNNs). Although GNNs
are powerful, accurately modeling the complex interactions between atoms and
molecules within a crystal remains a challenge. Surprisingly, predicting
crystal properties from crystal text descriptions is understudied, despite the
rich information and expressiveness that text data offer. One of the main
reasons is the lack of publicly available data for this task. In this paper, we
develop and make public a benchmark dataset (called TextEdge) that contains
text descriptions of crystal structures with their properties. We then propose
LLM-Prop, a method that leverages the general-purpose learning capabilities of
large language models (LLMs) to predict the physical and electronic properties
of crystals from their text descriptions. LLM-Prop outperforms the current
state-of-the-art GNN-based crystal property predictor by about 4% in predicting
band gap, 3% in classifying whether the band gap is direct or indirect, and 66%
in predicting unit cell volume. LLM-Prop also outperforms a finetuned MatBERT,
a domain-specific pre-trained BERT model, despite having 3 times fewer
parameters. Our empirical results may highlight the current inability of GNNs
to capture information pertaining to space group symmetry and Wyckoff sites for
accurate crystal property prediction.