arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2107.05620cond-mat.mtrl-sciphysics.comp-ph
收藏

机器学习潜力使预测性$\textit{and}$易于处理的随机合金高通量筛选成为可能

Machine-learning potentials enable predictive $\textit{and}$ tractable high-throughput screening of random alloys

Max Hodapp, Alexander Shapeev

备注：
contains some additional validation of the training algorithm and the potentials
发表信息：
Phys. Rev. Materials 5, 113802 (2021)
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

摘要：

我们提出了一个自动化的程序计算堆垛层错能随机合金从大规模模拟使用矩张量势（MTP）的准确性密度泛函理论（DFT）。为此，我们开发了一种算法，用于在随机合金上训练MTP。在第一步中，我们的算法构建了一组约10000或更多的训练候选配置与50-100个原子，是代表在大规模模拟中发生的原子邻域。在第二步中，我们使用主动学习将这个集合减少到大约100个最不同的配置-计算DFT能量和力，并最终训练势。我们验证了我们的算法MoNbTa中熵合金的MTP再现DFT $\frac{1}{4}[111]$不稳定的堆垛层错能量在整个组成空间高达百分之几。与现有技术的方法相反，相干势近似（CPA）或特殊的准随机结构（SQS），我们的算法自然帐户松弛，不受DFT细胞大小的限制，并打开机会，有效地调查后续问题，如化学排序。在更广泛的意义上，我们的算法可以很容易地修改，以计算随机合金的相关属性，例如，失配体积，或晶界能。此外，它形成的基础上，一个有效的建设MTPs用于大规模模拟的多组分系统。

英文摘要：

We present an automated procedure for computing stacking fault energies in random alloys from large-scale simulations using moment tensor potentials (MTPs) with the accuracy of density functional theory (DFT). To that end, we develop an algorithm for training MTPs on random alloys. In the first step, our algorithm constructs a set of ~10000 or more training candidate configurations with 50-100 atoms that are representative for the atomic neighborhoods occurring in the large-scale simulation. In the second step, we use active learning to reduce this set to ~100 most distinct configurations - for which DFT energies and forces are computed and on which the potential is ultimately trained. We validate our algorithm for the MoNbTa medium-entropy alloy by showing that the MTP reproduces the DFT $\frac{1}{4}[111]$ unstable stacking fault energy over the entire compositional space up to a few percent. Contrary to state-of-the-art methods, e.g., the coherent potential approximation (CPA) or special quasi-random structures (SQSs), our algorithm naturally accounts for relaxation, is not limited by DFT cell sizes, and opens opportunities to efficiently investigate follow-up problems, such as chemical ordering. In a broader sense, our algorithm can be easily modified to compute related properties of random alloys, for instance, misfit volumes, or grain boundary energies. Moreover, it forms the basis for an efficient construction of MTPs to be used in large-scale simulations of multicomponent 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。

↑