Study the effect of Surface Defects on Hydrophobicity at Rare Earth Oxide–Water Interfaces using Molecular Dynamics Simulations driven by ab initio-based Neural Network Potentials

Yang Zhang, University of Illinois at Urbana-Champaign

Usage Details

Hydrophobic and superhydrophobic surfaces that are robust to harsh environments have immense potential to enhance the performance of a plethora of applications. However, the successful widespread commercialization of hydrophobic surfaces has been fraught with many challenges, the biggest challenge being the lack of mechanical, chemical, and thermal robustness.

Recent studies show rare earth oxides (REOs) are intrinsically hydrophobic and durable owing to their unique electronic structure. However, surface defects, like adatoms, and vacancies, are ubiquitous, which may change the wettability of REOs. Thus, in this project, we propose to investigate the influence of the defects on hydrophobicity and understand the mechanism governing wettability by employing first-principle quality neural network potentials combined with molecular dynamics (MD) simulation. Expensive computation cost makes it only suitable on a petascale machine like Blue Waters.

This project is significant for simulating and understanding the hydrophobicity of REOs at the molecular level and providing insights to identify ideal candidates with strong hydrophobicity when considering the presence of defects.