Project Details

Description

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.

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

Funding

  • National Science Foundation: $215,000.00

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