arXiv 2110.081362021-10-15cond-mat.mtrl-sciphysics.comp-ph

主动学习和分子动力学模拟寻找高熔点合金

Active learning and molecular dynamics simulations to find high melting temperature alloys

David E. Farache, Juan C. Verduzco, Zachary D. McClure, Saaketh Desai, Alejandro Strachan

备注：: 23 pages, 11 figures

摘要：

主动学习（AL）可以大大加速材料发现;它的力量已经在各种类型的材料和目标属性中显示出来。先前的努力已经使用机器学习模型来进行物理实验或基于物理的模拟的最佳选择。然而，后者的努力主要局限于使用电子结构计算和性能，可以在晶胞水平和可忽略的噪音。我们耦合AL与分子动力学模拟，以确定多个主成分合金（MPCA）的高熔化温度。通过nanoHUB的云计算服务的基础上，我们提出了一个完全自主的工作流程，有效地探索高维组成空间的MPCA。我们的特点是如何产生的随机性的模拟和用于选择模拟的采集功能的不确定性影响的方法的收敛性。有趣的是，我们发现，具有显著不确定性的相对较短的模拟可以用于有效地找到所需的合金，因为用于AL的随机森林模型平均了波动。

英文摘要：

Active learning (AL) can drastically accelerate materials discovery; its power has been shown in various classes of materials and target properties. Prior efforts have used machine learning models for the optimal selection of physical experiments or physics-based simulations. However, the latter efforts have been mostly limited to the use of electronic structure calculations and properties that can be obtained at the unit cell level and with negligible noise. We couple AL with molecular dynamics simulations to identify multiple principal component alloys (MPCAs) with high melting temperatures. Building on cloud computing services through nanoHUB, we present a fully autonomous workflow for the efficient exploration of the high dimensional compositional space of MPCAs. We characterize how uncertainties arising from the stochastic nature of the simulations and the acquisition functions used to select simulations affect the convergence of the approach. Interestingly, we find that relatively short simulations with significant uncertainties can be used to efficiently find the desired alloys as the random forest models used for AL average out fluctuations.

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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英文摘要：

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