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Explores the evolution of biomolecular simulations, emphasizing accurate models, increased sampling, and the transformative role of simulations in predicting experimental outcomes.
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 history, mathematical models, and experimental techniques of Brownian Motion, revealing its molecular nature and significance in cell biology.
