Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells

Ziji Zhang, Peng Zhang, Changnian Han, Guojing Cong, Chih Chieh Yang, Yuefan Deng

Research output: Contribution to journalArticlepeer-review


We developed a biomechanics-informed online learning framework to learn the dynamics with ground truth generated with multiscale modeling simulation. It was built on Summit-like supercomputers, which were also used to benchmark and validate our framework on one physiologically significant modeling of deformable biological cells. We generalized the century-old equation of Jeffery orbits to a new equation of motion with additional parameters to account for the flow conditions and the cell deformability. Using simulation data at particle-based resolutions for flowing cells and the learned parameters from our framework, we validated the new equation by the motions, mostly rotations, of a human platelet in shear blood flow at various shear stresses and platelet deformability. Our online framework, which surrogates redundant computations in the conventional multiscale modeling by solutions of our learned equation, accelerates the conventional modeling by three orders of magnitude without visible loss of accuracy.

Original languageEnglish (US)
Article number812248
JournalFrontiers in Molecular Biosciences
StatePublished - Jan 27 2022


  • computational fluid dynamics
  • equation of motion
  • molecular dynamics
  • multiscale modeling
  • online machine learning

ASJC Scopus subject areas

  • Molecular Biology
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Biochemistry


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