Purdue University Graduate School
Adam_Garrett_Thesis_Final.pdf (2.56 MB)

Machine Learning and Molecular Dynamics Simulations of Thermal Transport

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posted on 2021-07-28, 22:56 authored by Adam Sandor Garland GarrettAdam Sandor Garland Garrett

The need for sources of efficient and renewable energy has become an issue of great importance in recent years. Fossil fuels are diminishing in supply, and they not only pollute the environment, but have also proven to be inefficient in many cases, losing a large portion of the total energy generated to waste heat, instead of usable energy.

The first work this thesis addresses is the development of a Genetic Algorithm (GA) optimization method for the search and discovery of semiconductor materials for use in thermoelectric devices. The specific material in question is the Silicon Germanium superlattice. This structure made of alternating layers of Si and Ge is known to be one of the better materials for thermoelectric energy generation at elevated temperature, along with Bismuth-Telluride that targets room temperature. Previously, it has been shown that random multilayer (RML) structures can lower thermal conductivity as compared to periodic superlattices due to phonon localization. However, it was unknown which specific RML would yield the lowest thermal conductivity, due to the large design space from which these RML’s can be generated. Considering this, a global and non-smooth optimization method was employed to search for the best possible structure. Results not only showed that the thermal conductivity could be lowered even further, but that there was an optimal average period for the RML’s that produced the best results.

The second work discussed in this thesis concerns itself with the development of a Neural Network Potential (NNP) for use in Molecular Dynamics (MD) simulations. There are multiple methods for running MD including ab-initiomethods such as Density Functional Theory (DFT) calculations and classical MD with the use of empirical potentials. Unfortunately, DFT is too time consuming for systems larger than a few hundred atoms, and empirical potentials can be inaccurate. Therefore, a NNP for bulk Silicon trained on DFT was developed, and it was shown that the phonon dispersion for Si could be accurately reproduced.


Degree Type

  • Master of Science


  • Mechanical Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Xiulin Ruan

Additional Committee Member 2

Amy Marconnet

Additional Committee Member 3

Guang Lin

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