On-line course on Big Data and Artificial Intelligence in Materials Sciences
Organised by NOMAD
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".
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 |
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| November 5, 2020 | General Introduction to big-data-driven materials science |
Matthias Scheffler (Fritz-Haber-Institut der MPG, Berlin & Humboldt-Universität zu Berlin) |
| November 12, 2020 | NOMAD Repository, Archive, Encyclopedia | Claudia Draxl (Humboldt-Universität zu Berlin & Fritz-Haber-Institut der MPG, Berlin) |
| November 19, 2020 | Introduction to artificial intelligence and machine-learning methods |
Luca Ghiringhelli (Fritz-Haber-Institut der MPG, Berlin) |
| November 26, 2020 | Finding patterns in high dimensional representations: unsupervised learning | Luigi Sbailò (Fritz-Haber-Institut der MPG, Berlin) |
| December 3, 2020 | Decision trees and random forests | Daniel Speckhard |
| December 10, 2020 | Regularized Regression and kernel methods | Santiago Rigamonti (Humboldt-Universität zu Berlin) |
| December 17, 2020 | Compressed sensing meets symbolic regression: SISSO | Luca Ghiringhelli (Fritz-Haber-Institut der MPG, Berlin) |
| January 7, 2021 | Artificial Neural Networks and Deep Learning | Angelo Ziletti (Bayer AG) |
| January 14, 2021 | Materials data, 4V, FAIR principles | Claudia Draxl (Humboldt-Universität zu Berlin & Fritz-Haber-Institut der MPG, Berlin) |
| January 21, 2021 | Subgroup discovery, rare-phenomena challenge, and domain of applicability |
Matthias Scheffler (Fritz-Haber-Institut der MPG, Berlin & Humboldt-Universität zu Berlin) |
| January 28, 2021 | Cluster expansion | Santiago Rigamonti (Humboldt-Universität zu Berlin) |
| February 4, 2021 | Interpretability and Causality | Jilles Vreeken* (CISPA Helmholtz Center for Information Security & MPI for Informatics, Saarbrücken) |
| February 11, 2021 | Applications in real materials | Rampi Ramprasad* (Georgia Institute of Technology, Atlanta) |
| February 18, 2021 | AI in experiment | Christoph T. Koch* (Humboldt-Universität zu Berlin) |
| 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 |
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| November 12, 2020 | NOMAD Repository and Archive | Markus Scheidgen (Humboldt-Universität zu Berlin) |
| November 26, 2020 | NOMAD Artificial Intelligence Toolkit I, Data Analytics on Archive data |
Luca Ghiringhelli (Fritz-Haber-Institut der MPG, Berlin) |
| December 10, 2020 | NOMAD Encyclopedia | Lauri Himanen (Aalto University) |
| 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) |