The Hitchhiker's Guide to Condensed Matter and Statistical Physics - Machine Learning for Condensed Matter

Explore machine learning applications in condensed matter and statistical physics through this comprehensive lecture series from ICTP. Learn to compute thermodynamic and transport properties using machine learning potentials, delving into the fundamental concepts and practical implementations. Discover how machine learning techniques can be applied to many-body quantum physics problems, understanding both theoretical foundations and computational approaches. Master the use of recurrent neural networks for analyzing complex many-body physical systems, gaining insights into how these advanced neural network architectures can capture temporal dependencies and correlations in quantum systems. Develop practical skills in applying cutting-edge machine learning methods to solve challenging problems in condensed matter physics, from basic computational techniques to sophisticated neural network implementations for quantum many-body systems.