Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells - Zhang, Ziji; Zhang, Peng; Han, Changnian; Cong, Guojing; Yang, Chih-Chieh; Deng, Yuefan (Frontiers in Molecular Biosciences, 2022)
We developed a biomechanics-informed online learning framework to learn the dynamics with ground truth generated with multiscale modeling simulation on the Summit-like supercomputers with which we also 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 cells’ 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.