Exploration of the Application of Machine Learning to the Improvement of Interatomic Potentials
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Current methods for atomistic simulations of material systems suffer from limitations which restrict the ability of the simulations to correctly characterize certain material behavior and physical phenomena. Small scale ab initio molecular dynamics (AIMD) modeling is highly accurate but is computationally expensive. Classical molecular dynamics (CMD) simulations use interatomic potentials (IPs) to describe larger systems at a reduced computational cost, but with a reduced accuracy. Creating more robust IPs enables more precise CMD simulations. In this thesis, the application of a specific machine learning process, artificial neural networks (ANNs), to the improvement of IPs and MD simulation is discussed.
Table of Contents
Introduction -- Background -- Methods -- Results and analysis -- conclusions and future work -- Appendix A. Program files -- Appendix B. Computing specifications
M.S. (Master of Science)