Instructor: 

Professor Cecilia Clementi <cecilia.clementi@fu-berlin.de>

Tutors:

Dr. Lorenzo Giambagli

Zeno Schätzle

Lecture time:

Tuesday 10:00-12:00

Wednesday 10:00-12:00

Lecture location: 

The lectures will be in person and will be held in 0.1.01 Lecture Hall B (Arnimallee 14). 

Tutorial location and time:

All tutorials will be in person and will be held on Tuesday 12:00-14:00 in room 1.4.03 Seminarraum T2 (Arnimallee 14).

The repository for the tutorials is located at https://github.com/ClementiGroup/AdvCompPhy2526

Please create a GitHub account (not Gitlab) and send your username (not account email) to klara.bonneau@fu-berlin.de to be granted access to the repository.

The course will cover:

- computational methods for electronic structure calculations

- molecular dynamics and Monte Carlo methods

- machine learning and applications in computational Physics

 

Lecture Schedule (tentative) 

01. Tuesday, October 14

Introduction

Tutorial: Python introduction

02. Wednesday, October 15

Solving the Schrödinger equation for complex molecular systems

Hartree-Fock methods

03. Tuesday, October 21

Quantum Monte Carlo methods

Related Tutorial

04. Wednesday, October 22

Quantum Monte Carlo methods

05. Tuesday, October 28

Density Functional Theory

Related Tutorial

06. Wednesday, October 29

Density Functional Theory

07. Tuesday, November 4

Molecular dynamics: force fields

Related Tutorial

08. Wednesday, November 5

Molecular dynamics: integrators

09. Tuesday, November 11

Molecular Dynamics: electrostatic treatment

Related Tutorial

10. Wednesday, November 12

Molecular Dynamics: implicit & explicit solvent methods

11. Tuesday, November 18

Molecular Dynamics: Langevin Dynamics

Related Tutorial

12. Wednesday, November 19

Molecular Dynamics: kinetics & MSM

13. Tuesday, November 25

Monte Carlo methods

Related Tutorial

14. Wednesday, November 26

Intro to machine learning: primer on statistics

15. Tuesday, December 2

Intro to machine learning

Related Tutorial

16. Wednesday, December 3

Deep Learning Architectures 

17. Tuesday, December 9

Deep Learning Architectures 

Related Tutorial

18. Wednesday, December 10

Dimensionality reduction and reaction coordinates

19. Tuesday, December 16

ML potentials

Related Tutorial

20. Wednesday, December 17

ML potentials

21. Tuesday, January 6

ML potentials

Related Tutorial

22. Wednesday, January 7

ML for coarse-graining

23. Tuesday, January 13

Generative models

Related Tutorial

24. Wednesday, January 14

Generative models

25., Tuesday, January 20

Physical Interpretation of ML Models

Related Tutorial

26. Wednesday, January 21

Physical Interpretation of ML Models

27. Tuesday, January 27

Deep Quantum Monte Carlo

Related Tutorial

28. Wednesday, January 28

Deep Quantum Monte Carlo

29. Tuesday, February 3

Project Development

Related Tutorial

30. Wednesday, February 4

Project Development

31. Tuesday, February 10 

Project presentations

Related Tutorial