Crystallization Machine Learning - Theory and Simulation Techniques

Explore the intersection of crystallization and machine learning in this comprehensive lecture from Advanced Cyberinfrastructure Training at RPI. Delve into theoretical foundations, including molecular dynamics and empirical models, with a focus on water and Tip3p water models. Examine density functional theory and electronic density of states before transitioning to machine learning applications in crystallization. Investigate initial machine learning methodologies and address the timescale problem in simulations. Learn about rare events, enhanced sampling techniques, and metadynamics. Conclude with an in-depth look at homogeneous crystal nucleation and associated simulation techniques. Gain valuable insights into cutting-edge approaches for understanding and predicting crystallization processes through advanced computational methods.