Active-learning-based efficient prediction of ab-initio atomic energy: a case study on a Fe random grain boundary model with millions of atoms
Active-learning-based efficient prediction of ab-initio atomic energy: a case study on a Fe random grain boundary model with millions of atoms
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发表时间：
2019-12
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通讯作者：
T. Tamura;Masayuki Karasuyama
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作者：
T. Tamura;Masayuki Karasuyama
我们已经开发出一种方法，可以分析大的随机晶界（GB）模型的密度泛函理论（DFT）计算的准确性，使用主动学习。假定原子能量由原子结构描述子的线性回归表示。原子能是通过DFT计算获得的，使用从巨大的GB模型中提取的小单元，称为副本DFT原子能。主动学习中的不确定性降低（UR）方法用于有效地收集原子能的训练数据。在这种方法中，不需要原子能来搜索候选点;因此，不需要连续的DFT计算。这种方法适用于可以同时执行大量作业的大规模并行计算机。在这项研究中，我们展示了预测的Fe随机GB模型包含一百万个原子使用UR方法的原子能量，并表明预测误差降低更迅速相比，随机采样。我们的结论是，UR的方法与副本DFT原子能是有用的建模巨大的GB和建模其他结构缺陷将是必不可少的。
We have developed a method that can analyze large random grain boundary (GB) models with the accuracy of density functional theory (DFT) calculations using active learning. It is assumed that the atomic energy is represented by the linear regression of the atomic structural descriptor. The atomic energy is obtained through DFT calculations using a small cell extracted from a huge GB model, called replica DFT atomic energy. The uncertainty reduction (UR) approach in active learning is used to efficiently collect the training data for the atomic energy. In this approach, atomic energy is not required to search for candidate points; therefore, sequential DFT calculations are not required. This approach is suitable for massively parallel computers that can execute a large number of jobs simultaneously. In this study, we demonstrate the prediction of the atomic energy of a Fe random GB model containing one million atoms using the UR approach and show that the prediction error decreases more rapidly compared with random sampling. We conclude that the UR approach with replica DFT atomic energy is useful for modeling huge GBs and will be essential for modeling other structural defects.