Machine Learning-Based Modeling of Spreading Dynamics in Complex Interfacial Systems

Understanding droplet interactions with surfaces is crucial for large-scale oil recovery, coating industries, and microfluidic devices. To address the remaining challenges, including the need for a universal timescale and improved theoretical and computational models, this project brings together the SoftSimu group at Western University and the Soft Materials Modelling (SMM) group at Aalto University, Finland. The SoftSimu group, led by Prof. Karttunen, specializes in advanced molecular simulations and applying machine learning to bridge theoretical models with practical applications, particularly in elucidating droplet spreading process. The SMM group, a founding member of the Academy of Finland National Center of Excellence in Life-Inspired Hybrid Materials (LIBER), excels in modeling soft materials from the atomistic to mesoscale, leveraging the advanced LUMI supercomputer. In collaboration with experimental groups at LIBER, they have demonstrated the importance of molecular simulations in designing super-slippery coatings. The project aims to (1) develop expertise in simulating surfactant-laden droplets and SAMs-decorated surfaces, (2) enhance daily collaboration with experimental groups at LIBER, and (3) share expertise in modeling wetting dynamics using machine learning. Through this collaborative effort, we aim to advance the fundamental understanding and practical applications of droplet dynamics, ultimately contributing to the development of more effective and innovative technologies.

Faculty Supervisor:

Mikko Karttunen

Student:

Partner:

Aalto University

Discipline:

Physics

Sector:

Water; Environmental Science and Technology; Nanotechnology

University:

The University of Western Ontario

Program: