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Materials property prediction using symmetry-labeled graphs as atomic position independent descriptors

机译：材料性能预测使用对称标记的图形作为原子位置独立描述符

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摘要

Computational materials screening studies require fast calculation of the properties of thousands of materials. The calculations are often performed with density functional theory (DFT), but the necessary computer time sets limitations for the investigated material space. Therefore, the development of machine-learning models for prediction of DFT-calculated properties is currently of interest. A particular challenge for new materials is that the atomic positions are generally not known. We present a machine-learning model for the prediction of DFT-calculated formation energies based on Voronoi quotient graphs and local symmetry classification without the need for detailed information about atomic positions. The model is implemented as a message passing neural network and tested on the Open Quantum Materials Database (OQMD) and the Materials Project Database. The test mean absolute error is 22 meV on the OQMD and 43 meV on Materials Project Database. The possibilities for prediction in a realistic computational screening setting are investigated on a data set of 5976 ABSe(3) selenides with very limited overlap with the OQMD training set. Pretraining on OQMD and subsequent training on 100 selenides result in a mean absolute error below 0.1 eV for the formation energy of the selenides.
机译：计算材料筛查研究需要快速计算成千上万材料的性质。通常用密度泛函理论（DFT）进行计算，但是必要的计算机时间为调查的材料空间设定限制。因此，用于预测DFT计算的属性的机器学习模型是目前的感兴趣的。对新材料的特殊挑战是原子位置通常不知道。我们为基于Voronoi商品图和局部对称分类提供了一种用于预测DFT计算的形成能量的机器学习模型，而无需了解原子位置的详细信息。该模型实现为传递神经网络的消息，并在打开量子材料数据库（OQMD）和材料项目数据库上进行测试。测试平均值误差是OQMD和43 MeV的22 MeV在材料项目数据库上。在具有非常有限的与OQMD训练集的数据集的数据集上研究了现实计算筛选设置中预测的可能性。对OQMD的预先预订和随后的100种硒化族培训导致硒化物的形成能量低于0.1eV的平均绝对误差。

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