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.

开发出一个名为 TextEdge 的基准数据集，包含了关于晶体结构及其性质的文本描述，并提出了一种名为 LLM-Prop 的方法，利用大型语言模型（LLMs）的通用学习能力，从晶体的文本描述中预测其物理和电子性质，并在预测带隙、分类带隙是否为直接或间接和预测晶胞体积方面的性能优于现有最先进的基于 GNN 的晶体性质预测模型。

LLM-Prop: 从文本描述预测晶体固体的物理和电子性质

LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline  Solids From Their Text Descriptions

Materials discovery, especially for applications that require extreme
operating conditions, requires extensive testing that naturally limits the
ability to inquire the wealth of possible compositions. Machine Learning (ML)
has nowadays a well established role in facilitating this effort in systematic
ways. The increasing amount of available accurate DFT data represents a solid
basis upon which new ML models can be trained and tested. While conventional
models rely on static descriptors, generally suitable for a limited class of
systems, the flexibility of Graph Neural Networks (GNNs) allows for direct
learning representations on graphs, such as the ones formed by crystals. We
utilize crystal graph neural networks (CGNN) to predict crystal properties with
DFT level accuracy, through graphs with encoding of the atomic (node/vertex),
bond (edge), and global state attributes. In this work, we aim at testing the
ability of the CGNN MegNet framework in predicting a number of properties of
systems previously unseen from the model, obtained by adding a substitutional
defect in bulk crystals that are included in the training set. We perform DFT
validation to assess the accuracy in the prediction of formation energies and
structural features (such as elastic moduli). Using CGNNs, one may identify
promising paths in alloy discovery.

本文利用晶体图神经网络（CGNN）预测水晶的属性，通过对原子、结合和整体状态特征的编码，在 DFT 的级别上实现预测的正确性，从而在合金探索中找到有前途的路径。