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The transport of biomolecules across bio-membranes occurs in a complex environment where the fluid on both sides of the membrane contains many inclusions. The Monte Carlo method and the hard-sphere (HS) model are used to simulate the translocation of linear polymer and ring polymer through a nanopore in a crowded environment. We compare the results of linear polymer and ring polymer and find that the ring polymer is more sensitive to the surrounding environment. Moreover, the influences of the nanopore and the inclusions to the translocation are studied and our results show that the nanopore changes the translocation time and the inclusions change the translocation tendency to the random side of the membrane. Here, the radius of gyration is described as a balance.

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We study the XY model on a spherical surface by using Monte Carlo simulations and machine learning methods. Instead of a traditional latitude-longitude lattice, we consider a much more homogeneous spherical lattice, the Fibonacci lattice. After obtaining the equilibrium spin configurations by classical Monte Carlo annealing, we propose a graph-convolutional-network based method to recognize different phases, and successfully predict the phase transition temperature. We also apply the density-based spatial clustering of applications with noise, a powerful machine learning algorithm, to unravel the merging path of two vortices with different topological charges on the sphere. Our results provide reliable predictions for future space-based experiments on ultracold atomic gases confined on spherical lattice in the microgravity environment.