用于材料发现的半监督师生深度神经网络
Semi-supervised teacher-student deep neural network for materials discovery
摘要：
数据驱动的生成式机器学习模型最近成为发现新材料的最有前途的方法之一。虽然生成器模型可以生成数百万个候选项，但训练快速准确的机器学习模型以筛选出具有所需特性的稳定的可合成材料至关重要。然而，这种建立监督回归或分类筛选模型的努力由于缺乏不稳定或不可合成的样本而受到严重阻碍，这些样本通常不会收集并存放在ICSD和材料项目（MP）等材料数据库中。与此同时，这些数据库中还有大量未加标签的数据。在这里，我们提出了一个半监督深度神经网络（TSDNN）模型，用于高性能的形成能量和可合成性预测，这是通过其独特的师生双网络架构和有效利用大量未标记数据来实现的。对于基于地层能量的稳定性筛选，与基线CGCNN回归模型相比，我们的半监督分类器实现了绝对10.3\%的准确性提高。对于可合成性预测，我们的模型使用1/49模型参数将基线PU学习的真阳性率从87.9%显著提高到97.9%。为了进一步证明我们的模型的有效性，我们将TSDNN能量和TSDNN可合成性模型与CubicGAN生成器相结合，以发现新的稳定立方结构。在我们的模型推荐的1000个候选样品中，其中512个具有负的形成能，我们的DFT形成能计算验证了这一点。我们的实验结果表明，我们的半监督深度神经网络可以显着提高大规模生成材料设计中的筛选精度。
英文摘要：
Data driven generative machine learning models have recently emerged as one of the most promising approaches for new materials discovery. While the generator models can generate millions of candidates, it is critical to train fast and accurate machine learning models to filter out stable, synthesizable materials with desired properties. However, such efforts to build supervised regression or classification screening models have been severely hindered by the lack of unstable or unsynthesizable samples, which usually are not collected and deposited in materials databases such as ICSD and Materials Project (MP). At the same time, there are a significant amount of unlabelled data available in these databases. Here we propose a semi-supervised deep neural network (TSDNN) model for high-performance formation energy and synthesizability prediction, which is achieved via its unique teacher-student dual network architecture and its effective exploitation of the large amount of unlabeled data. For formation energy based stability screening, our semi-supervised classifier achieves an absolute 10.3\% accuracy improvement compared to the baseline CGCNN regression model. For synthesizability prediction, our model significantly increases the baseline PU learning's true positive rate from 87.9\% to 97.9\% using 1/49 model parameters. To further prove the effectiveness of our models, we combined our TSDNN-energy and TSDNN-synthesizability models with our CubicGAN generator to discover novel stable cubic structures. Out of 1000 recommended candidate samples by our models, 512 of them have negative formation energies as validated by our DFT formation energy calculations. Our experimental results show that our semi-supervised deep neural networks can significantly improve the screening accuracy in large-scale generative materials design.
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