Platja d’Aro, Spain, September 9 – 13, 2019
Webpage: https://th.fhi-berlin.mpg.de/meetings//BiGmax-Summer-2019/
Organizers:
Gerhard Dehm
Max-Planck-Institut für Eisenforschung, Düsseldorf, Germany
Claudia Draxl
Humboldt-Universität zu Berlin, Berlin, Germany
Matthias Scheffler
Fritz Haber Institute of the Max Planck Society, Berlin, Germany
Jilles Vreeken
CISPA – Helmholtz Center for Information Security, Germany
Scope:
Materials science is entering an era where the growth of data from experiments and simulations is expanding beyond a level that is addressable by established scientific methods. The so-called “4 V challenge” – concerning Volume (the amount of data), Variety (the heterogeneity of form and meaning of data), Velocity (the rate at which data may change or new data arrive), and Veracity (uncertainty of quality) is clearly becoming eminent. Issues are, for example, an early discrimination between valuable and irrelevant experimental data, understanding errors in both experiment and theory, and assigning error bars and trust levels to density-functional theory high-throughput screening results, just to name a few. Most importantly, however, is that Big Data of materials science provide a significant chance for new insight and knowledge gain when fully exploiting its information by artificial intelligence concepts and methods. All the above aspects – from data processing to exploiting the potentials of data-driven materials science – require new and dedicated approaches.
The school was predominantly targeted towards PhD students and young postdocs. The 15 invited speakers addressed important background and recent advances in data-driven materials science. The topics covered a wide spectrum to demonstrate the challenges and potential that research data offer, including:
- FAIR principles of scientific data, including hardware aspects
- introduction and frontiers of artificial intelligence
- interpretability and causality in machine learning
- various data-mining tools and mathematical concepts behind
- data diagnostics
- pattern discovery
- real-time data processing of emerging experimental setups
- metadata in computational and experimental materials science.
You can find slides from the invited speakers here: https://th.fhi-berlin.mpg.de/meetings/BiGmax-Summer-2019/index.php?n=Meeting.Program
Program:
Monday, September 9, 2019
| 15:00 | Arrival – Coffee break | |
| Session chair: Claudia Draxl | ||
| 15:30 – 16:30 | Matthias Scheffler | Welcome and Introduction |
| 16:30 – 17:30 | Jilles Vreeken | Material Subgroups |
| 17:30 – 18:00 | Break | |
| 18:00 – 19:00 | Hans-Joachim Bungartz | Research Data Infrastructures – How Generic Can & Should They Be? |
| 19:30 | Welcome Cocktail – Dinner | |
Tuesday, September 10, 2019
| 08:00 | Breakfast | |
| Session chair: Hans-Joachim Bungartz | ||
| 09:00 – 10:00 | Claudia Draxl | The NOMAD Encyclopedia – A Tool for Exploring Computed Data |
| 10:00 – 11:00 | Dierk Raabe | Big Data-Related Challenges in Microstructure Research and Alloy Design |
| 11:00 – 11:30 | Break | |
| 11:30 – 12:30 | Siyuan Zhang | Modern Electron Microscopy Goes High Dimension: Handling Big Data |
| 12:30 – 12:50 | Hot Topic Talk: Raabe (Atomic-Scale Imaging of Chemistry at Lattice Defects) | |
| 13:00 – 15:00 | Lunch Break | |
| Session chair: Matthias Scheffler | ||
| 15:00 – 16:00 | Joseph F. Rudzinski | Data-Driven Methods for Soft Matter |
| 16:00 – 16:20 | Hot Topic Talk: Vreeken (Telling Cause from Effect) | |
| 16:20 – 16:50 | Break | |
| 16:50 – 20:00 | Poster Parade and Poster Session | |
| 20:00 | Dinner | |
Wednesday, September 11, 2019
| 08:00 | Breakfast | |
| Session chair: Isao Tanaka | ||
| 09:00 – 09:45 | Markus Rampp | High-Performance Data Analytics: Basic Concepts of Distributed Deep Learning |
| 09:45 – 10:45 | Karsten W. Jacobsen | Machine Learning and Computational Screening |
| 10:45 – 11:15 | Break | |
| 11:15 – 12:15 | Luca M. Ghiringhelli | Metadata Towards FAIR Data Sharing for Data-Driven Materials Science |
| 12:15 – 12:55 | Hot Topic Talks:
– Jacobsen (High Entropy Alloys for Catalysis) – Draxl (Benchmark Calculations Towards Ultimate Precision in Density-Functional Theory) |
|
| 13:00 – 15:00 | Lunch break | |
| Session chair: Karsten W. Jacobsen | ||
| 15:00 – 16:00 | Cécile Hébert | Data Challenges in Analytical Transmission Electron Microscopy: Size, Formats and Annotation |
| 16:00 – 16:40 | Hot Topic Talks:
– Ghiringhelli (Identifying Interpretable Descriptors for Materials Properties with Subgroup Discovery and Information Theory) – Rudzinski (Variational Autoencoders for Dimensionality Reduction and Clustering of Molecular Dynamics Data) |
|
| 16:40 – 17:20 | Break | |
| 17:20 – 18:20 | Annette Trunschke | Big-Data Driven Catalysis Research: Challenges and Chances |
| 18:20 – 18:40 | Hot Topic Talk: Hébert (Machine Learning Techniques in Analytical TEM: Trends and Challenges) | |
| 20:00 | Dinner | |
Thursday, September 12, 2019
| 08:00 | Breakfast | |
| Session chair: Stefan Bauer | ||
| 09:00 – 10:00 | Chiho Kim | Polymer Informatics: Past, Present and Future |
| 10:00 – 10:40 | Hot Topic Talks:
– Bauer (Learning Disentangled Representations) – Tanaka (Data Driven Discovery of New Materials) |
|
| 10:40 – 11:10 | Break | |
| 11:10 – 12:10 | Luca M. Ghiringhelli | Learning Descriptors for Materials Properties with Symbolic Regression and Compressed Sensing |
| 12:10 – 12:30 | Hot Topic Talk: Trunschke (Clean Data Acquisition in Oxidation Catalysis) | |
| 13:00 | Lunch break | |
| 14:30 | Excursion and Conference Dinner | |
Friday, September 13, 2019
| 8:00 | Breakfast | |
| Session chair: Matthias Scheffler | ||
| 09:00 – 10:00 | Stefan Bauer | Recent Advances in Unsupervised Representation Learning |
| 10:00 – 11:00 | Isao Tanaka | Recommender System for Materials Discovery |
| 11:00 | Concluding remarks | |