| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Computation Assisted Materials Development for Improved Corrosion Resistance
|
| Presentation Title |
Predicting Hydrogen Diffusivity in Amorphous Titania Using Markov Chain Kinetic Monte Carlo Simulations |
| Author(s) |
James Chapman, Kyoung E Kweon, Nir Goldman, Nathan Keilbart, Tae Wook Heo, Brandon C Wood |
| On-Site Speaker (Planned) |
James Chapman |
| Abstract Scope |
Understanding hydrogen transport is vital to industries focused on discovering new materials for corrosion mitigation. These materials typically have three diffusion-limiting structural domains: (1) grain interior (2) grain boundaries, and (3) surfaces. Here, we aim to understand diffusion through titania grain boundaries, which are approximated via the amorphous phase. Density functional theory was used to calculate thousands of activation energies of hydrogen hopping. Large-scale amorphous structures were generated using a machine learning force field via molecular dynamics (MD). Kinetic Monte Carlo (KMC) simulations were then performed on the MD-generated structures, allowing us to connect directly with experimental measurements. Using our KMC simulations we can directly calculate the hydrogen diffusion constant, as a function of temperature and stoichiometry, and compare these values with those determined via experiments. This work sets the stage to better understand how temperature and local atomic structure affect properties such as diffusivity, solubility, and permeation. |