基于机器学习的石墨烯负泊松比的原子间相互作用势
Accessing negative Poisson`s ratio of graphene by machine learning interatomic potentials
摘要：
负泊松比（NPR）是材料的一种新特性，它增强了材料的力学性能，在航空航天、电子、医学等领域有着广泛的应用前景。对负泊松比机理的基本认识对于设计先进的机械功能材料具有重要作用。然而，使用不同的方法，发现NPR的起源不同，并且相互冲突，例如，在代表性的石墨烯中。本文基于机器学习技术，构造了一个用于石墨烯分子动力学模拟的矩张量势。通过分析关键几何构型的演变，发现键角的增加是石墨烯NPR的原因，而不是键长的增加。关于NPR起源的结果与最新的第一性原理是一致的，第一性原理修正了用经典经验势进行MD模拟的结果。我们的研究有助于理解石墨烯的NPR起源，并为提高MD模拟的准确性铺平了道路，可以与第一性原理计算相媲美。该研究也将促进机器学习原子间相互作用势在功能材料多尺度模拟中的应用。* 作者
英文摘要：
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
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