Allgemeine Angaben 

Computational Materials Design   




lecture with integrated exercises 












Allocations: 1  
eLearning[Provide new moodle course in current semester] 



Angaben zur Abhaltung 

This course is the first step toward the paradigm shift of rational materials design from purely theoretical methods based on physical laws to a hybridation with automated learning strategies. Basic quantum chemical theories would be introduced with a special focus on Density Functional Theory. Then the theories would be applied to predict some fundamental properties of materials using standard software packages. Following this the basics of machine learning would be introduced along with some handson applications. Machine learning techniques would then be applied to predict material properties using training data obtained by quantum mechanical calculations. The potential of designing materials with desirably properties using a combination of these two approaches would be explored. 


The focus is on grad students (Open to Physics, Chemistry and Materials Science students also) Basic programming Helpful: Quantum mechanics, solid state physics 


After successful completion of the module, students will
 understand the basics of quantumchemical (QC) theories, with a special focus on DensityFunctionalTheory (DFT)
 know which material properties can be predicted with QC/DFTmethods
be able to use abinitio software to model said material properties be familiar with classical machine learning (ML) techniques and their theoretical foundations
 apply MLtechniques to simple datasets and evaluate the quality of the model
 gained insight into the current state of MLbased techniques for material property prediction 




The course consists of weekly lectures and exercises. In the lecture the module contents will be presented by the teacher, supported by an electronic presentation. During the exercises, students will do handson calculations with numerical simulation programs/machine learning tools. In addition, there will be a final project.
eLearning material on TUMMoodle will be shared when the course is ongoing.   presentation  exercises solving computational problems 




Für die Anmeldung zur Teilnahme müssen Sie sich in TUMonline als Studierende*r identifizieren. 


Zusatzinformationen 

Some small readings will be suggested during the course and will be announced in class
 Martin, R.M.: Electronic Structure. Basic Theory and Practical Methods. Cambridge University Press, 2004 [doi.org/10.1017/CBO9780511805769]
 James, G. et al.: An Introduction to Statistical Learning. Springer, 2013 [http://wwwbcf.usc.edu/gareth/ISL
 Feliciano Giustino: Materials Modelling using Density Functional Theory 




