FAIR-DI e.V.
FAIRmat
NOMAD Laboratory
NOMAD CoE

On-line course on Big Data and Artificial Intelligence in Materials Sciences

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On-line course on Big Data and Artificial Intelligence in Materials Sciences

 

In the winter term 2020/21, we offer an on-line course, which includes extensive hands-on sessions, on "Big Data and Artificial Intelligence in Materials Sciences", aimed to the students of Max Planck Graduate Center for Quantum Materials.
The course covers all the hottest topics in artificial intelligence, including machine and deep learning, compressed sensing, and data mining. General and specific AI concepts are introduced, always with a focus on Materials Science applications, in particular the design and discovery of improved, new, and novel materials for contemporary and future technological advances.

The course is designed to have a strong interactive character. Besides weekly lectures, also by international guest lecturers, it includes four extended hands-on exercises, based on the infrastructure of the NOMAD Laboratory, thus enabling the participants for a smooth transition from learning the basic principles to apply them to analyze the largest database of high-quality Materials-Science data.

 
Program:
Lectures
1 November 5, 2020 General Introduction to big-data-driven materials science

Matthias Scheffler

(Fritz-Haber-Institut der MPG, Berlin & Humboldt-Universität zu Berlin)

2 November 12, 2020 NOMAD Repository, Archive, Encyclopedia

Claudia Draxl

(Humboldt-Universität zu Berlin & Fritz-Haber-Institut der MPG, Berlin)

3 November 19, 2020 Introduction to artificial intelligence and machine-learning methods

Luca Ghiringhelli

(Fritz-Haber-Institut der MPG, Berlin)

4 November 26, 2020 Finding patterns in high dimensional representations: unsupervised learning

Luigi Sbailò

(Fritz-Haber-Institut der MPG, Berlin)

5 December 3, 2020 Decision trees and random forests Daniel Speckhard
6 December 10, 2020 Regularized Regression and kernel methods

Santiago Rigamonti

(Humboldt-Universität zu Berlin)

7 December 17, 2020 Compressed sensing meets symbolic regression: SISSO

Luca Ghiringhelli

(Fritz-Haber-Institut der MPG, Berlin)

8 January 7, 2021 Artificial Neural Networks and Deep Learning

Angelo Ziletti

(Bayer AG)

9 January 14, 2021 Materials data, 4V, FAIR principles

Claudia Draxl

(Humboldt-Universität zu Berlin & Fritz-Haber-Institut der MPG, Berlin)

10 January 21, 2021 Subgroup discovery, rare-phenomena challenge, and domain of applicability

Matthias Scheffler

(Fritz-Haber-Institut der MPG, Berlin & Humboldt-Universität zu Berlin)

11 January 28, 2021 Cluster expansion

Santiago Rigamonti

(Humboldt-Universität zu Berlin)

12 February 4, 2021 Interpretability and Causality

Jilles Vreeken*

(CISPA Helmholtz Center for Information Security & Max Planck Institute for Informatics, Saarbrücken)

13 February 11, 2021 Applications in real materials

Rampi Ramprasad*

(Georgia Institute of Technology, Atlanta)

14 February 18, 2021 AI in experiment

Christoph T. Koch*

(Humboldt-Universität zu Berlin)

15 February 25, 2021 Fusion of experimental and computational data by AI

Lucas Foppa

(Fritz-Haber-Institut der MPG, Berlin)

 * to be confirmed

 

Hands-on exercises
1 November 12, 2020 NOMAD Repository and Archive

Markus Scheidgen

(Humboldt-Universität zu Berlin)

2 November 26, 2020 NOMAD Artificial Intelligence Toolkit I, Data Analytics on Archive data

Luca Ghiringhelli

(Fritz-Haber-Institut der MPG, Berlin)

3 December 10, 2020 NOMAD Encyclopedia

Lauri Himanen

(Aalto University)

4 January 7, 2020 NOMAD Artificial Intelligence Toolkit II, hands-on SISSO, subgroup discovery, random forests, neural networks, and more

Luca Ghiringhelli

(Fritz-Haber-Institut der MPG, Berlin)