Virtual materials screening approaches have proliferated in the past decade, driven by rapid advances in first-principles computational techniques, and machine-learning algorithms. By comparison, computationally driven materials synthesis screening is still in its infancy, and is mired by the challenges of data sparsity and data scarcity: Synthesis routes exist in a sparse, high-dimensional parameter space that is difficult to optimize over directly, and, for some materials of interest, only scarce volumes of literature-reported syntheses are available. In this article, we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes. We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space, which is found to improve the performance of machine-learning tasks. To realize this screening framework even in cases where there are few literature data, we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems. We apply this variational autoencoder framework to generate potential SrTiO₃ synthesis parameter sets, propose driving factors for brookite TiO₂ formation, and identify correlations between alkali-ion intercalation and MnO₂ polymorph selection.

在第一原理计算技术和机器学习算法的飞速发展的推动下，虚拟材料筛选方法在过去十年中得到了广泛应用。相比之下，以计算为驱动的材料合成筛选仍处于起步阶段，并被数据稀疏性和数据稀缺性的挑战所困扰。：合成路线存在于稀疏的高维参数空间中，很难直接进行优化，对于某些感兴趣的材料，只有很少数量的文献报道的合成方法可用。在本文中，我们提出了一个框架，用于建议定量合成参数和合成结果的潜在驱动因素。我们使用变分自动编码器将稀疏的合成表示压缩到较低维度的空间中，从而提高了机器学习任务的性能。为了即使在文献数据很少的情况下也要实现这种筛选框架，我们设计了一种新颖的数据扩充方法，该方法结合了来自相关材料系统的文献综合数据。我们应用此变体自动编码器框架来生成潜在的SrTiO ₃合成参数集，提出板钛矿TiO ₂形成的驱动因素，并确定碱金属离子插层与MnO ₂多晶型物选择之间的相关性。