Login / Signup

Efficient compressed database of equilibrated configurations of ring-linear polymer blends for MD simulations.

Katsumi HagitaTakahiro MurashimaMasao OginoManabu OmiyaKenji OnoTetsuo DeguchiHiroshi JinnaiToshihiro Kawakatsu
Published in: Scientific data (2022)
To effectively archive configuration data during molecular dynamics (MD) simulations of polymer systems, we present an efficient compression method with good numerical accuracy that preserves the topology of ring-linear polymer blends. To compress the fraction of floating-point data, we used the Jointed Hierarchical Precision Compression Number - Data Format (JHPCN-DF) method to apply zero padding for the tailing fraction bits, which did not affect the numerical accuracy, then compressed the data with Huffman coding. We also provided a dataset of well-equilibrated configurations of MD simulations for ring-linear polymer blends with various lengths of linear and ring polymers, including ring complexes composed of multiple rings such as polycatenane. We executed 10 9 MD steps to obtain 150 equilibrated configurations. The combination of JHPCN-DF and SZ compression achieved the best compression ratio for all cases. Therefore, the proposed method enables efficient archiving of MD trajectories. Moreover, the publicly available dataset of ring-linear polymer blends can be employed for studies of mathematical methods, including topology analysis and data compression, as well as MD simulations.
Keyphrases
  • molecular dynamics
  • density functional theory
  • electronic health record
  • big data
  • emergency department
  • data analysis
  • monte carlo
  • artificial intelligence