Self-supervised generative models for crystal structures

作者：Fangze Liu, Zhantao Chen, Tianyi Liu, Ruyi Song, Yu Lin, Joshua J. Turner, Chunjing Jia · 发表于：iScience · 年份：2024 · DOI：10.1016/j.isci.2024.110672 · 被引用次数：4 · 研究领域：Machine Learning in Materials Science、X-ray Diffraction in Crystallography、Computational Drug Discovery Methods

Inspired by advancements in natural language processing, we utilize self-supervised learning and an equivariant graph neural network to develop a unified platform for training generative models capable of generating inorganic crystal structures, as well as efficiently adapting to downstream tasks in material property prediction. To mitigate the challenge of evaluating the reliability of generated structures during training, we employ a generative adversarial network (GAN) with its discriminator being a cost-effective reliability evaluator, significantly enhancing model performance. We demonstrate the utility of our model in optimizing crystal structures under predefined conditions. Without external properties acquired experimentally or numerically, our model further displays its capability to help understand inorganic crystal formation by grouping chemically similar elements. This paper extends an invitation to further explore the scientific understanding of material structures through generative models, offering a fresh perspective on the scope and efficacy of machine learning in material science.