论文标题
通过机器学习间原子势访问石墨烯的负泊松比
Accessing negative Poisson`s ratio of graphene by machine learning interatomic potentials
论文作者
论文摘要
负泊松比（NPR）是材料的新型特性，它增强了机械特征，并在许多领域（例如航空航天，电子，药物等）中创造了广泛的应用前景。对NPR基础机制的基本理解在设计高级机械功能材料中起着重要作用。但是，通过使用不同的方法，NPR的起源被发现不同，并且相互冲突，例如，在代表性石墨烯中。在这项研究中，基于机器学习技术，我们构建了石墨烯分子动力学（MD）模拟的力矩张量电势（MTP）。通过分析关键几何形状的演变，发现键角的增加是造成石墨烯的NPR而不是键长的原因。关于NPR起源的结果与艺术初期原理非常一致，该原理使用经典的经验潜能修改了MD模拟的结果。我们的研究促进了对石墨烯NPR起源的理解，并为提高MD模拟的准确性的方式铺平了与第一原则计算相媲美的方法。我们的研究还将在功能材料的多尺度模拟中促进机器学习间潜能的应用。 *作者
The negative Poisson`s ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental understanding on the mechanism underlying NPR plays an important role in designing advanced mechanical functional materials. However, with different methods used, the origin of NPR is found different and conflicting with each other, for instance, in the representative graphene. In this study, based on machine learning technique, we constructed a moment tensor potential (MTP) for molecular dynamics (MD) simulations of graphene. By analyzing the evolution of key geometries, the increase of bond angle is found to be responsible for the NPR of graphene instead of bond length. The results on the origin of NPR are well consistent with the start-of-art first-principles, which amend the results from MD simulations using classic empirical potentials. Our study facilitates the understanding on the origin of NPR of graphene and paves the way to improve the accuracy of MD simulations being comparable to first-principle calculations. Our study would also promote the applications of machine learning interatomic potentials in multiscale simulations of functional materials. *Author
