Hands-on DFT and Beyond: Accuracy, Efficiency, and Reproducibility in Computational Materials Science


Humboldt University, Berlin, Germany, July 31 to August 11, 2017 

The discovery of novel materials is key on the route to face global challenges like the quest for efficient and sustainable use of energy resources. Computational approaches play a central role here as they allow us to explore uncharted territory in chemical and materials space, for example in order to develop novel batteries, highly efficient solar cells, stable biocatalysts, or carbon dioxide fixation strategies. 

Novel Materials Discovery by Learning from Electronic-Structure Theory is the central theme of this summer school, we will teach the basics and recent advances of electronic-structure theory. 

The focus is in particular on density-functional theory (DFT), but also topics beyond DFT will be covered: ab initio thermodynamics and statistical mechanics, excited-state properties, nuclear quantum effects, multi-scale modeling, and machine learning approaches to potential parametrization, Big-Data dimensionality reduction, and property prediction. 


Program and additional Materials

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Video Resources


1   Materials discovery from electronic structure Matthias Scheffler


2   Practical implementations of DFT I: Technical foundations and numerical methods Volker Blum
3   Exchange-correlation functionals Hardy Gross 
4   Approaches to van der Waals interactions Alexandr Tkatchenko
5   Practical implementations of DFT II: SCF, forces, and structure optimization Oliver Hofmann


6   Periodic systems: Concepts and numeric atom-centered orbitals Sergey Levchenko
7   Plane-wave pseudopotentials and projector augmented wave methods Xavier Gonze
8   Augmented plane-wave methods Claudia Draxl
10   Basics and state of the art of quantum-chemistry methods for molecules, clusters, and materials Igor Ying Zhang


9   Reproducibility in density-functional theory calculations of solids Stefaan Cottenier
11   Spectroscopic benchmark datasets for molecules Thomas Koerzdoerfer
12   Sampling and searching the conformational space of molecules Carsten Baldauf

Time and length scales

13   Electronic-structure theory for large-scale simulations Lin Lin
14   Ab initio statistical mechanics Luca Ghiringhelli
15   Ab initio molecular dynamics Mariana Rossi

Machine learning

16   Machine learning for materials modelling Gabor Csanyi
17   Machine learning for quantum mechanics Matthias Rupp
18   Compressed sensing and screening for properties Luca Ghiringhelli

Wavelets, statistical mechanics, and cluster expansion

19   DFT with wavelets Stefan Goedecker
20   Ab initio thermodynamics Sergey Levchenko
21   Kinetic Monte Carlo Peter Kratzer
22   Cluster expansion Santiago Rigamonti

High-throughput calculations, embedding quantum chemistry, and GW

23   After you have learned everything about DFT-energy, you discover that you need entropy! Stefano Curtarolo
24   Embedding quantum regions in classical environments Harald Oberhofer
25   Many-body and GW Patrick Rinke

Transport and Reactions

26   Electronic transport Ferdinand Evers
27   Phonons, electron-phonon coupling, and transport in solids Christian Carbogno

The Next Frontiers

29   Dimensionality reduction for Big-Data analytics Michele Ceriotti
30   Excited states and BSE Claudia Draxl
31   Quantum Monte Carlo and exascale computing Paul Kent