In this section we present the details on simulations we performed. Initially N + 2 graphene layers of size L × L are placed along z axis with d = 3.35Å spacing between each other. Followed is the water block of size L × L × W and N + 2 more graphene layers of the same size. The resulting system is mirrored with respect to a XY plane and shifted along Z such that distance d between graphene layers of the original system and its symmetric image is maintained. The schematic of the setup is shown in Fig. 1 of the Main Text.Each simulation consists of three stages: equilibration to isothermal-isobaric (N, P, T ) ensemble; applying temperature gradient at canonical ensemble (N, V, T ); collecting the statistics at canonical ensemble while the temperature gradient is maintained. To achieve the temperature gradient, a high temperature Nosé-Hoover heat bath is applied to four central graphene layers and a low temperature Nosé-Hoover heat bath is applied to four outermost graphene layers (two leftmost and two rightmost). Periodic boundary conditions are applied in all directions. Equilibration always takes 400 ps. Applying temperature gradient takes from 1 ns to 3 ns depending on the number of used layers, and collecting the statistics takes from 2 ns to 3 ns.Water was modeled with flexible simple point charge (SPC) model [1]. We also perform a simulation using rigid SPC water model [2]. The result are almost identical to the corresponding simulation with flexible water model: see Table S1.Typical pressure evolution versus time is shown in the Fig. S3. High pressure oscillations are observed due to the high stiffness of both water and graphene. However, the average pressure remains constant. DATA EXTRACTIONKapitza resistance R K was calculated as R K = ∆T /J, where ∆T is the temperature jump at the solid-liquid interface, and J is the heat flux through the interface. The heat flux is defined as the conducted energy from the high temperature heat bath to the low temperature heat sink per unit time across unit area. Due to the symmetry of our simulation setup (Fig. 1), the heat flux can be computed as half of the slope of the energy change with respective to time in the heat bath: J = 0.5 d dt ∆E(t), where ∆E is induced energy in heat bath (Fig. S2). In our calculations, we average the local temperature in two symmetric copies. As shown in Fig. S1, ∆T is computed from the solid and liquid temperature at the interface. Solid temperature at the interface is determined by a linear fitting of the temperatures in different graphene layers. Water is divided into bins of 0.1Å in thickness along z, thus liquid temperature at the interface is determined by fitting the temperatures of water in different bins on a straight line. The error in Kapitza resistance is given by errors of two linear fits.Important to note that with the described procedure, we calculate two Kapitza resistances -one at a high temperature interface, and the other at a low temperature interface (Fig. S1). In all our simulation, the low temperature Kapitza resis...

Schooling, an archetype of collective behavior, emerges from the interactions of fish responding to visual and other informative cues mediated by their aqueous environment. In this context, a fundamental and largely unexplored question concerns the role of hydrodynamics. Here, we investigate schooling by modeling swimmers as vortex dipoles whose interactions are governed by the Biot-Savart law. When we enhance these dipoles with behavioral rules from classical agent based models we find that they do not lead robustly to schooling due to flow mediated interactions. In turn, we present dipole swimmers equipped with adaptive decision-making that learn, through a reinforcement learning algorithm, to adjust their gaits in response to non-linearly varying hydrodynamic loads. The dipoles maintain their relative position within a formation by adapting their strength and school in a variety of prescribed geometrical arrangements. Furthermore, we identify schooling patterns that minimize the individual and the collective swimming effort, through an evolutionary optimization. The present work suggests that the adaptive response of individual swimmers to flow-mediated interactions is critical in fish schooling.Comment: 18 pages, 12 figure

We study the fluid dynamics of two fish-like bodies with synchronised swimming patterns. Our studies are based on two-dimensional simulations of viscous incompressible flows. We distinguish between motion patterns that are externally imposed on the swimmers and self-propelled swimmers that learn manoeuvres to achieve certain goals. Simulations of two rigid bodies executing pre-specified motion indicate that flow-mediated interactions can lead to substantial drag reduction and may even generate thrust intermittently. In turn we examine two self-propelled swimmers arranged in a leader-follower configuration, with a-priori specified body-deformations. We find that the swimming of the leader remains largely unaffected, while the follower experiences either an increase or decrease in swimming speed, depending on the initial conditions. Finally, we consider a follower that synchronises its motion so as to minimize its lateral deviations from the leader's path. The leader employs a steady gait while the follower uses a reinforcement learning algorithm to adapt its swimming-kinematics. We find that swimming in a synchronised tandem can yield up to about 30% reduction in energy expenditure for the follower, in addition to a 20% increase in its swimming-efficiency. The present results indicate that synchronised swimming of two fish can be energetically beneficial.

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We present simulations of blood and cancer cell separation in complex microfluidic channels with subcellular resolution, demonstrating unprecedented time to solution, performing at 65.5% of the available 39.4 PetaInstructions/s in the 18, 688 nodes of the Titan supercomputer. These simulations outperform by one to three orders of magnitude the current state of the art in terms of numbers of simulated cells and computational elements. The computational setup emulates the conditions and the geometric complexity of microfluidic experiments and our results reproduce the experimental findings. These simulations provide sub-micron resolution while accessing time scales relevant to engineering designs. We demonstrate an improvement of up to 45X over competing state-of-the-art solvers, thus establishing the frontiers of simulations by particle based methods. Our simulations redefine the role of computational science for the development of microfluidics-a technology that is becoming as important to medicine as integrated circuits have been to computers.

The transport and manipulation of particles and cells in microfluidic devices has become a core methodology in domains ranging from molecular biology to manufacturing and drug design. The rational design and operation of such devices can benefit from simulations that resolve flow-structure interactions at sub-micron resolution. We present a computational tool for large scale, efficient and high throughput mesoscale simulations of fluids and deformable objects at complex microscale geometries. The code employs Dissipative Particle Dynamics for the description of the flow coupled with visco-elastic membrane model for red blood cells and can also handle rigid bodies and complex geometries. The software (MiRheo) is deployed on hybrid GPU/CPU architectures exhibiting unprecedented time-to-solution performance and excellent weak and strong scaling for a number of benchmark problems. MiRheo exploits the capabilities of GPU clusters, leading to speedup of up to 10X in terms of time to solution as compared to state-of-the-art software packages and reaches 90% -99% weak scaling efficiency on 512 nodes of the Piz Daint supercomputer. The software MiRheo, relies on a Python interface to facilitate the solution of complex problems and it is open source. We believe that MiRheo constitutes a potent computational tool that can greatly assist studies of microfluidics.

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