Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

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Sample mixing is difficult in microfluidic devices because of laminar flow. Micromixers are designed to ensure the optimal use of miniaturized devices. The present study aims to design a chaotic-advection-based passive micromixer with enhanced mixing efficiency. A serpentine-shaped microchannel with sinusoidal side walls was designed, and three cases, with amplitude to wavelength (A/λ) ratios of 0.1, 0.15, and 0.2 were investigated. Numerical simulations were conducted using the Navier–Stokes equations, to determine the flow field. The flow was then coupled with the convection–diffusion equation to obtain the species concentration distribution. The mixing performance of sinusoidal walled channels was compared with that of a simple serpentine channel for Reynolds numbers ranging from 0.1 to 50. Secondary flows were observed at high Reynolds numbers that mixed the fluid streams. These flows were dominant in the proposed sinusoidal walled channels, thereby showing better mixing performance than the simple serpentine channel at similar or less mixing cost. Higher mixing efficiency was obtained by increasing the A/λ ratio.