Statistical learning of reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics
Abstract/Contents
- Abstract
- Complex chemical processes, such as the decomposition of energetic materials, the chemistry of planetary interiors, and surface catalysis, are typically studied using large-scale molecular dynamics simulations that can run for weeks on high performance parallel machines. These computations may involve thousands of atoms forming hundreds of molecular species and undergoing thousands of reactions. It is natural to wonder whether this wealth of data can be utilized to build more efficient, interpretable, and predictive models. In this thesis, we use techniques from statistical learning to develop a framework for constructing kinetic Monte Carlo (KMC) models from molecular dynamics data. We show that our KMC models can not only extrapolate the behavior of the chemical system by as much as an order of magnitude in time, but can also be used to study the dynamics of entirely different chemical trajectories with a high degree of fidelity. We also develop a new and efficient data-driven method for reducing our learned KMC models using L1-regularization. We describe how our L1-regularization based algorithm can also be applied to complex systems of reaction rate equations such as those studied in the combustion community, providing a novel data-driven method for reducing nonlinear dynamical systems.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Yang, Qian |
|---|---|
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering. |
| Primary advisor | Reed, Evan J |
| Thesis advisor | Reed, Evan J |
| Thesis advisor | Cai, Wei, 1962- |
| Thesis advisor | Candès, Emmanuel J. (Emmanuel Jean) |
| Advisor | Cai, Wei, 1962- |
| Advisor | Candès, Emmanuel J. (Emmanuel Jean) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Qian Yang. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Qian Yang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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