Pattern mining and visualization for molecular dynamics simulation

Molecular dynamics is a computer simulation technique for expressing the
ultimate details of individual particle motions and can be used in many fields, such as
chemical physics, materials science, and the modeling of biomolecules. In this thesis, we
study visualization and pattern mining in molecular dynamics simulation. The molecular
data set has a large number of atoms in each frame and range of frames. The features of
the data set include atom ID; frame number; position in x, y, and z plane; charge; and
mass. The three main challenges of this thesis are to display a larger number of atoms and
range of frames, to visualize this large data set in 3-dimension, and to cluster the
abnormally shifting atoms that move with the same pace and direction in different frames.
Focusing on these three challenges, there are three contributions of this thesis. First, we
design an abnormal pattern mining and visualization framework for molecular dynamics
simulation. The proposed framework can visualize the clusters of abnormal shifting atom
groups in a three-dimensional space, and show their temporal relationships. Second, we propose a pattern mining method to detect abnormal atom groups which share similar
movement and have large variance compared to the majority atoms. We propose a
general molecular dynamics simulation tool, which can visualize a large number of atoms,
including their movement and temporal relationships, to help domain experts study
molecular dynamics simulation results. The main functions for this visualization and
pattern mining tool include atom number, cluster visualization, search across different
frames, multiple frame range search, frame range switch, and line demonstration for atom
motions in different frames. Therefore, this visualization and pattern mining tool can be
used in the field of chemical physics, materials science, and the modeling of
biomolecules for the molecular dynamic simulation outcomes.