This paper highlights a shift in how to approach material generation. Instead of material-to-material, we propose a language-to-material generation architecture that utilizes millions of untapped data points. Using a web scraper to collect crystal text pairs from open-source research papers, a contrastive model can be trained using a convolutional graph neural network encoder and a language encoder. This would allow unsupervised zero-shot classification which can be trained by taking advantage of linguistic structure. Without any specific training data, an ~82\% accuracy was achieved and ~75\% accuracy for photocatalyst prediction with an extremely small dataset. This novel network could ideally be cross-applied to any reaction that can be described via text, opening completely new methods to think about 3D chemical framework generation. In the full experiment diffusion models would likely be incorporated to fully exploit the latent space.

通过建立一种语言到材料生成的架构，利用未利用的大量数据，本文介绍了一种新的材料生成方法。使用网络爬虫从开源研究论文中收集水晶文本对，并使用卷积图神经网络编码器和语言编码器来训练对比模型。通过利用语言结构，可以进行零样本分类，实验结果表明在无具体训练数据的情况下，可获得约82％的准确率和约75％的光催化剂预测准确率，且该网络方法可以应用于任何可以通过文本描述的化学反应，为三维化学框架生成提供了全新的思路。

CLAMP：对比式自然语言和分子预训练网络