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Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Explores Car-Parrinello molecular dynamics, a unified approach combining molecular dynamics and density-functional theory for simulating various systems, with a focus on historical background, technical details, and challenges in atomistic simulations.
Explores the trends and challenges in modeling complex molecular systems using hierarchical multi-scale approaches, covering length-time scales, atomistic simulations, and force matching techniques.
