Identification of durable hydrophobic inorganic materials using molecular dynamics simulations driven by ab initio-based neural network potenitals

Yang Zhang, University of Illinois at Urbana-Champaign

Usage Details

Hydrophobic and superhydrophobic surfaces, which repel liquid water and result in equilibrium solid/liquid contact angles of >90° and >150°, have immense potential to enhance the performance of a plethora of applications. From controlling water retention and drainage, to enhancing phase change heat transfer, to reducing ice adhesion and energy input for defrosting, to eliminating fouling, scaling, and corrosion.

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. Inorganic materials have much higher mechanical strength and temperature stability compared to organic materials. Nonetheless, little is known about their wettability and interaction with water at solid-liquid interface given many kinds of alloys and compounds available. Thus, in this project, we propose to identify inorganic candidate materials for creation of durable hydrophobicity employing first-principle quality neural network potentials combined with molecular dynamics (MD) simulation.