论文标题
通过原子模拟和机器学习的非平等高渗透合金的机械性能预测
Prediction of mechanical properties of non-equiatomic high-entropy alloy by atomistic simulation and machine learning
论文作者
论文摘要
由于其有前途的工程应用，在过去20年中，具有多种组成元素的高渗透合金（HEAS）进行了广泛的研究。 HEAS的先前的实验和计算研究主要集中在等型或近距离HEAS上。但是，在那些具有精心设计成分的非平等性heas中，可能会有更多的宝藏。在这项研究中，分子动力学（MD）模拟与机器学习（ML）方法相结合来预测非平等cufenicrco heas的机械性能。基于MD模拟的900个HEA单晶样品的拉伸测试建立了数据库。我们研究并比较了八种学习任务的ML模型，从浅层模型到深层模型。发现基于内核的极限学习机（KELM）模型优于其他人的预测屈服应力和杨氏模量。 KELM模型的准确性通过大型多晶Hea样品进一步验证。
High-entropy alloys (HEAs) with multiple constituent elements have been extensively studied in the past 20 years due to their promising engineering application. Previous experimental and computational studies of HEAs focused mainly on equiatomic or near equiatomic HEAs. However, there is probably far more treasure in those non-equiatomic HEAs with carefully designed composition. In this study, molecular dynamics (MD) simulation combined with machine learning (ML) methods were used to predict the mechanical properties of non-equiatomic CuFeNiCrCo HEAs. A database was established based on a tensile test of 900 HEA single-crystal samples by MD simulation. We investigated and compared eight ML models for the learning tasks, ranging from shallow models to deep models. It was found that the kernel-based extreme learning machine (KELM) model outperformed others for the prediction of yield stress and Young's modulus. The accuracy of the KELM model was further verified by the large-sized polycrystal HEA samples.
