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arXiv 1806.04777cond-mat.mtrl-sci
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Ni-Mo二元合金和FCC金属的量子精确光谱近邻分析势模型

Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and FCC Metals

Xiang-Guo Li, Chongze Hu, Chi Chen, Zhi Deng, Jian Luo, Shyue Ping Ong

摘要:

近年来,已经开发了接近密度泛函理论(DFT)计算精度的有效原子间势,其使用满足严格不变性的严格原子描述符,例如,对于同质原子的平移、旋转、排列等。本文将光谱近邻分析势(SNAP)模型推广到bcc-fcc二元合金体系。我们证明,机器学习的SNAP模型可以产生显着的改善,甚至超过完善的,高性能的嵌入原子方法(EAM)和改进的EAM(MEAM)的fcc铜和镍的潜力。我们还报告了一个SNAP模型的fcc Ni-bcc Mo二元系统的机器学习精心构造的元素和金属间化合物的大型计算数据集的发展。我们证明了该二元Ni-Mo SNAP模型在预测Ni-Mo相图时可以达到与实验非常好的一致性,并且在预测许多关键性质如弹性常数、形成能、熔点等时可以达到接近DFT的精度,在整个二进制组成范围内。相比之下,现有的Ni-Mo EAM在相图的预测中具有显著的误差,并且在二元化合物中完全失败。这项工作为多组分合金系统提供了一个系统的模型开发过程,包括一个有效的程序来优化模型拟合中的超参数,并为此类系统的长时间,大规模模拟铺平了道路。

英文摘要:

In recent years, efficient inter-atomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, to translation, rotation, permutation of homonuclear atoms, among others. In this work, we generalize the spectral neighbor analysis potential (SNAP) model to bcc-fcc binary alloy systems. We demonstrate that machine-learned SNAP models can yield significant improvements even over well-established, high-performing embedded atom method (EAM) and modified EAM (MEAM) potentials for fcc Cu and Ni. We also report on the development of a SNAP model for the fcc Ni-bcc Mo binary system by machine learning a carefully-constructed large computed data set of elemental and intermetallic compounds. We demonstrate that this binary Ni-Mo SNAP model can achieve excellent agreement with experiments in the prediction of Ni-Mo phase diagram as well as near-DFT accuracy in the prediction of many key properties such as elastic constants, formation energies, melting points, etc., across the entire binary composition range. In contrast, the existing Ni-Mo EAM has significant errors in the prediction of the phase diagram and completely fails in binary compounds. This work provides a systematic model development process for multicomponent alloy systems, including an efficient procedure to optimize the hyper-parameters in the model fitting, and paves the way to long-time, large-scale simulations of such systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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