Identifier
etd-04092015-224028
Degree
Doctor of Philosophy (PhD)
Department
Computer Science
Document Type
Dissertation
Abstract
Molecular dynamics simulation method is widely used to calculate and understand a wide range of properties of materials. A lot of research efforts have been focused on simulation techniques but relatively fewer works are done on methods for analyzing the simulation results. Large-scale simulations usually generate massive amounts of data, which make manual analysis infeasible, particularly when it is necessary to look into the details of the simulation results. In this dissertation, we propose a system that uses computational method to automatically perform analysis of simulation data, which represent atomic position-time series. The system identifies, in an automated fashion, the micro-level events (such as the bond formation/breaking) that are connected to large movements of the atoms, which is considered to be relevant to the diffusion property of the material. The challenge is how to discover such interesting atomic activities which are the key to understanding macro-level (bulk) properties of material. Furthermore, simply mining the structure graph of a material (the graph where the constituent atoms form nodes and the bonds between the atoms form edges) offers little help in this scenario. It is the patterns among the atomic dynamics that may be good candidate for underlying mechanisms. We propose an event-graph model to model the atomic dynamics and propose a graph mining algorithm to discover popular subgraphs in the event graph. We also analyze such patterns in primitive ring mining process and calculate the distributions of primitive rings during large and normal movement of atoms. Because the event graph is a directed acyclic graph, our mining algorithm uses a new graph encoding scheme that is based on topological- sorting. This encoding scheme also ensures that our algorithm enumerates candidate subgraphs without any duplication. Our experiments using simulation data of silica liquid show the effectiveness of the proposed mining system.
Date
2015
Document Availability at the Time of Submission
Release the entire work immediately for access worldwide.
Recommended Citation
Shakya, Shobhit Sandesh, "Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data" (2015). LSU Doctoral Dissertations. 2034.
https://repository.lsu.edu/gradschool_dissertations/2034
Committee Chair
Zhang, Jian
DOI
10.31390/gradschool_dissertations.2034