Abstract：
Foundations of nanofluidics can enable advances in diverse applications such as water desalination, energy harvesting, and biological analysis. Dynamically manipulating nanofluidic properties, such as diffusion and friction, is an area of great scientific interest. Twisted bilayer graphene, particularly at the magic angle, has garnered attention for its unconventional superconductivity and correlated insulator behavior due to strong electronic correlations. The impact of the electronic properties of moiré patterns in twisted bilayer graphene on structural and dynamic properties of water remains largely unexplored. Computational challenges, stemming from simulating large unit cells using density functional theory, have hindered progress. This study addresses this gap by investigating water behavior on twisted bilayer graphene, employing a deep neural network potential (DP) model trained with a data set from ab initio molecular dynamics simulations. It is found that as the twisted angle approaches the magic angle, interfacial water friction increases, leading to a reduced water diffusion. Notably, the analysis shows that at smaller twisted angles with larger moiré patterns, water is more likely to reside in AA stacking regions than AB (or BA) stacking regions, a distinction that diminishes with smaller moiré patterns. This study illustrates the potential for leveraging the distinctive properties of moiré systems to effectively control and optimize interfacial fluid behavior.
摘要：
纳米流体的基础可以在各种应用中取得进展，如水淡化，能量收集，生物分析。动态操纵纳米流体特性，比如扩散和摩擦，是一个非常有科学兴趣的领域。扭曲双层石墨烯，特别是在神奇的角度，由于强烈的电子相关性，其非常规超导性和相关的绝缘体行为引起了人们的注意。扭曲双层石墨烯中莫尔图案的电子性质对水的结构和动态性质的影响仍未被探索。计算挑战，源于使用密度泛函理论模拟大单位细胞，阻碍了进展。本研究通过研究扭曲双层石墨烯上的水行为来解决这一差距，采用深度神经网络电位（DP）模型，使用来自从头算分子动力学模拟的数据集进行训练。发现当扭曲角接近魔角时，界面水摩擦增加，导致水扩散减少。值得注意的是,分析表明，在较小的扭曲角度和较大的莫尔图案下，与AB(或BA)堆叠区域相比，水更可能存在于AA堆叠区域，随着较小的莫尔图案而减少的区别。这项研究说明了利用莫尔系统的独特特性来有效控制和优化界面流体行为的潜力。
