Efficient solutions of self-consistent mean field equations for dewetting and electrostatics in nonuniform liquids

We use a new configuration-based version of linear response theory to efficiently solve self-consistent mean field equations relating an effective single particle potential to the induced density. The versatility and accuracy of the method is illustrated by applications to dewetting of a hard sphere solute in a Lennard-Jones fluid, the interplay between local hydrogen bond structure and electrostatics for water confined between two hydrophobic walls, and to ion pairing in ionic solutions. Simulation time has been reduced by more than an order of magnitude over previous methods.Comment: Supplementary material included at end of main pape

A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications

Reply to: Mobility overestimation in MoS2_2 transistors due to invasive voltage probes

In this reply, we include new experimental results and verify that the observed non-linearity in rippled-MoS$_2$ (leading to mobility kink) is an intrinsic property of a disordered system, rather than contact effects (invasive probes) or other device issues. Noting that Peng Wu's hypothesis is based on a highly ordered ideal system, transfer curves are expected to be linear, and the carrier density is assumed be constant. Wu's model is therefore oversimplified for disordered systems and neglects carrier-density dependent scattering physics. Thus, it is fundamentally incompatible with our rippled-MoS$_2$, and leads to the wrong conclusion

Graphene in Ionic Liquids: Collective van der Waals Interaction and Hindrance of Self-Assembly Pathway

Over the past decade, there has been much controversy regarding the microscopic mechanism by which the π-electron-rich carbon nanomaterials such as graphene and carbon nanotubes can be dispersed in ionic liquids. Through a combination of a quantum mechanical calculation on the level of density functional theory, an extensive molecular dynamics study on the time scale of microseconds, and a kinetic analysis at the experimental time scale, we have demonstrated that collective van der Waals forces between ionic liquids and graphene are able to describe both the short-ranged cation−π interaction and the long-ranged dispersion interaction and this microscopic interaction drives two graphene plates trapped in their metastable state while two graphene plates easily self-assemble into graphite in water

Graphene in Ionic Liquids: Collective van der Waals Interaction and Hindrance of Self-Assembly Pathway

Over the past decade, there has been much controversy regarding the microscopic mechanism by which the π-electron-rich carbon nanomaterials such as graphene and carbon nanotubes can be dispersed in ionic liquids. Through a combination of a quantum mechanical calculation on the level of density functional theory, an extensive molecular dynamics study on the time scale of microseconds, and a kinetic analysis at the experimental time scale, we have demonstrated that collective van der Waals forces between ionic liquids and graphene are able to describe both the short-ranged cation−π interaction and the long-ranged dispersion interaction and this microscopic interaction drives two graphene plates trapped in their metastable state while two graphene plates easily self-assemble into graphite in water