论文标题
水晶双胞胎：晶体材料属性预测的自我监督学习
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction
论文作者
论文摘要
机器学习（ML）模型在材料特性的预测中已广泛成功。但是，训练准确的ML模型所需的大型标记数据集是难以捉摸的，并且计算量昂贵。在未标记数据上训练ML模型的自我监管学习（SSL）框架的最新进展已经减轻了这个问题，并在计算机视觉和自然语言处理任务中表现出了卓越的表现。从SSL中的发展中汲取灵感，我们引入了Crystal Twins（CT）：SSL材料属性预测的SSL方法。使用大型未标记的数据集，我们通过将冗余原理应用于从同一晶体系统获得的增强实例的图形潜在嵌入来预先培训图形神经网络（GNN）。通过对GNN进行回归任务时，分享预先训练的权重，我们可以显着提高7个具有挑战性的物质财产预测基准的性能
Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elusive and computationally expensive to generate. Recent advances in Self-Supervised Learning (SSL) frameworks capable of training ML models on unlabeled data have mitigated this problem and demonstrated superior performance in computer vision and natural language processing tasks. Drawing inspiration from the developments in SSL, we introduce Crystal Twins (CT): an SSL method for crystalline materials property prediction. Using a large unlabeled dataset, we pre-train a Graph Neural Network (GNN) by applying the redundancy reduction principle to the graph latent embeddings of augmented instances obtained from the same crystalline system. By sharing the pre-trained weights when fine-tuning the GNN for regression tasks, we significantly improve the performance for 7 challenging material property prediction benchmarks
