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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 the trends and challenges in modeling complex molecular systems using hierarchical multi-scale approaches, covering length-time scales, atomistic simulations, and force matching techniques.
Explores the predictive power of modelling reaction kinetics at low computational cost, emphasizing the importance of accurate models and efficient approximations.
Discusses the inclusion of fermented foods in global food guides, exploring their benefits and risks, enzyme kinetics, and the history of food enzymes.
