Responsible: Prof. Jens-Uwe Sommer
Hours per week (2 + 2)
Language: English

Materials: Exercises and Scripts

Part 1. Monte-Carlo Methods (Dr. Werner)

• Simple & importance sampling
• Markov chains, detailed balance & Metropolis algorithm
• Random walks and diffusion
• Molecules with excluded volume and the „blob“ concept
• Monte Carlo in various ensembles
• Methods for estimating free energies

Part 2. Machine Learning Methods (Dr. Werner)

• Regression and classification
• Artificial neural networks
• Recurrent and convolutional networks
• Autoencoders

Part 3. Molecular dynamics (MD) simulation (Dr. Merlitz)

• Classical mechanics
• Time integration schemes
• Typical softmatter models
• Thermo- and barostats
• Averaged properties
• Time-correlation functions
• Transport coefficients
• Brownian dynamics

Recommended literature:

Daan Frenkel & Berend Smit, Understanding Molecular Simulation, Academic Press (2002)
D. C. Rapaport, The Art of Molecular Dynamics Simulations, Cambridge University Press (2007).
M. E. J. Newman, G. T. Barkema, Monte Carlo Methods in Statistical Physics, Clarendon Press, Oxford (2001).
M. Rubinstein, R. Colby: Polymer Physics, Oxford University Press, Oxford (2004).
M. Doi, S.F. Edwards: The theory of polymer dynamics, Clarendon Press, Oxford (1986).

I. Goodfellow, Y. Bengio, A.Courville, "Deep Learning", MIT Press, Cambridge (2016) (http://www.deeplearningbook.org)
E. Alpaydın, Introduction to machine learning, MIT Press, Cambridge (2014)

SCMBP