09.10.2019

Are you interested in learning more about Computational Materials Design?

Then attend the new teaching course with lecture and tutorial exercise of Dr. Obaidur Rahaman on Wednesdays and Fridays.

General information
Title Computational Materials Design Number 00EINEU016
Type lecture with integrated exercises Semester weekly hours 5 Offered in Winter semester 2019/20
Lecturer (Assistant) Gagliardi, Alessio [L] , Rahaman, Obaidur
Organisation Assistant Professorship of Simulation of Nanosystems for Energy Conversion (Prof. Gagliardi) (Contact information)
Curriculum / ECTS Credits Details Course categories Allocations: 1
Course description
Content 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 hands-on 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. Previous Knowledge Expected
The focus is on grad students (Open to Physics, Chemistry and Materials Science students also)
Basic programming Helpful: Quantum mechanics, solid state physics Objective (Expected Results of Study and Acquired Competences)
After successful completion of the module, students will - understand the basics of quantum-chemical (QC) theories, with a special focus on Density-Functional-Theory (DFT) - know which material properties can be predicted with QC/DFT-methods -be able to use ab-initio software to model said material properties -be familiar with classical machine learning (ML) techniques and their theoretical foundations - apply ML-techniques to simple datasets and evaluate the quality of the model - gained insight into the current state of ML-based techniques for material property prediction
Languages of Instruction English Teaching and Learning Method (Transfer of Skills)
Workload for Students 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 hands-on calculations with numerical simulation programs/machine learning tools.
In addition, there will be a final project. e-Learning material on TUM-Moodle will be shared when the course is ongoing. ---- - presentation - exercises solving computational problems
Scheduled Dates Details Course Criteria & Registration
For registration you have to be identified in TUMonline as a student.
Further information
Recommended Reading
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://www-bcf.usc.edu/gareth/ISL - Feliciano Giustino: Materials Modelling using Density Functional Theory
Online Information Online information e-learning course
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