CAMLET: A Combined Ab-initio Manifold Learning Toolbox for Nanostructure Simulations

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AbstractMolecular Dynamics simulations are powerfultools to study problems of materials science, nanoscience, and biology. It naturally providesample opportunities for interdisciplinary researchthat 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 andquality of the ab-initio simulation results.Hongyuan Zha, Qiang Du, Runze Li and Jorge Sofo willinvestigate learning andcomputational methods 1) to characterize both the local and globalstructures of the low-dimensional manifold in which the simulationreally occurs through manifold learning from the trajectories of theab-initio simulation; 2) to identify and extract suitable clusters inthe reduced dimension spaces corresponding to regions in theconfiguration space that naturally emerge from the ab-initiosimulation and are visited frequently by the particles throughout thesimulation; 3) to conduct efficient energy and force interpolationusing Gaussian Kriging models with penalized likelihood. In thislearning and computationalframework, the interpolated potential energy surface will beevaluated and it will replace the costly ab-initio evaluation when itsprecision is good enough. As the simulation evolves, the interpolatedpotential energy surface will be retested to detect theeventual need of a retraining in case the simulation is exploringnew regions of the configuration space.