Project Details


Abstract Molecular Dynamics simulations are powerful tools to study problems of materials science, nanoscience, and biology. It naturally provides ample opportunities for interdisciplinary research that requires knowledge in mathematics, statistics, computer science, physics, materials and biology. The focus of this project is on developing learning-based computational and statistical methods for potential energy landscape modeling to accelerate ab-initio molecular dynamics simulations. The set of tools developed will substantially expand the limits of time and system size without compromising the precision and quality of the ab-initio simulation results. Hongyuan Zha, Qiang Du, Runze Li and Jorge Sofo will investigate learning and computational methods 1) to characterize both the local and global structures of the low-dimensional manifold in which the simulation really occurs through manifold learning from the trajectories of the ab-initio simulation; 2) to identify and extract suitable clusters in the reduced dimension spaces corresponding to regions in the configuration space that naturally emerge from the ab-initio simulation and are visited frequently by the particles throughout the simulation; 3) to conduct efficient energy and force interpolation using Gaussian Kriging models with penalized likelihood. In this learning and computational framework, the interpolated potential energy surface will be evaluated and it will replace the costly ab-initio evaluation when its precision is good enough. As the simulation evolves, the interpolated potential energy surface will be retested to detect the eventual need of a retraining in case the simulation is exploring new regions of the configuration space.

Effective start/end date10/1/049/30/08


  • National Science Foundation: $215,000.00
  • National Science Foundation: $215,000.00


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