Research on Condensed Matter Property Prediction Based on Large Language Model

This study proposes a condensed matter property prediction framework based on Large Language Model (LLM), which combines material crystal structure encoding with physical prior knowledge to solve the problems of high computational cost and low high-throughput screening efficiency in traditional Density Functional Theory (DFT). The system design includes a multi-scale feature extraction module (atomic level graph attention+lattice symmetry encoding) and an adaptive head for predicting physical properties, supporting end-to-end prediction of key physical properties such as elastic modulus and band gap. Experiments have shown that on the Materials Project dataset, the model's prediction error (MAE) for elastic modulus is 8.7 GPa (32 % lower than CGCNN), and the correlation coefficient (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{2}$</tex>) for semiconductor bandgap is 0.91 (28 % higher than SchNet). Key innovations include: 1) Crystal serialization representation method based on RoBERTa architecture; 2) Introduce a loss function with symmetry constraints. In the future, we will explore cross scale joint prediction of physical properties and closed-loop optimization of experimental data.