As an efficient calculation and screening method, high-throughput can discover and optimize new materials, shorten the development cycle and cost of new materials. However, using high throughput for material screening and computation, a large amount of computing resources and storage space are indispensable. To accelerate the design of novel superhard carbon materials, we combined machine learning methods with high-throughput computing to construct three machine learning models: support vector machine regression, random forests, and artificial neural networks, and mined data from existing material databases to select 1276 structures as datasets for the model to predict the volume modulus and shear modulus. Through comparative analysis, the optimal model was selected to predict the bulk modulus and shear modulus of the structures obtained by high-throughput calculations, and the prediction results of the model were verified by density functional theory (DFT) calculations, and 8 superhard carbon allotropes in the 2 space group were eventually found.

中文翻译：

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加速Pca21空间群新型超硬碳同素异形体的设计：高通量筛选和机器学习策略

高通量作为一种高效的计算和筛选方法，可以发现和优化新材料，缩短新材料的开发周期和成本。然而，利用高通量进行材料筛选和计算，需要大量的计算资源和存储空间。为了加速新型超硬碳材料的设计，我们将机器学习方法与高通量计算相结合，构建了三种机器学习模型：支持向量机回归、随机森林和人工神经网络，并从现有材料数据库中挖掘数据，选择了1276种材料。结构作为模型的数据集来预测体积模量和剪切模量。通过对比分析，选择最优模型对高通量计算得到的结构的体积模量和剪切模量进行预测，并通过密度泛函理论（DFT）计算和8种超硬碳同素异形体对模型的预测结果进行验证最终在2个空间群中被发现。