BiGmax Summer School 2021

Material Package

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Specific sessions

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Sessions*

Machine Learning - Theory & Application

Open Data

Learning from Complex Data

Machine Learning - Theory

Multidimensional Data Analysis

Concluding remarks

Poster Session: Machine Learning -Theory & Applications / Open Data

Poster Session II: Open Data

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Contributions*

A materials informatics framework to discover patterns in atom probe tomography data. (in session "Poster Session: Machine Learning -Theory & Applications / Open Data")

Deep generative models for the design of Dual phase steel microstructures (in session "Poster Session: Machine Learning -Theory & Applications / Open Data")

Dynamic structure investigation of biomolecules with pattern recognition algorithms and X-ray experiments (in session "Poster Session: Machine Learning -Theory & Applications / Open Data")

Learning Dynamics of STEM by Enforcing Physical Consistency with Phase-Field Models (in session "Poster Session: Machine Learning -Theory & Applications / Open Data")

Lecture "Artificial Intelligence and High-Performance Data Mining for Accelerating Scientific Discovery" (in session "Learning from Complex Data")

Lecture "Four Generations of Neural Network Potentials" (in session "Machine Learning - Theory")

Lecture "HyperSpy: theory and applications" (in session "Multidimensional Data Analysis")

Lecture "Machine Learning for Electron Microscopy: from Imaging to Atomic Fabrication" (in session "Machine Learning - Theory & Application")

Lecture "Machine-learning aided atom probe tomography: status and (possible) directions" (in session "Machine Learning - Theory & Application")

Lecture "Neural Networks with Euclidean Symmetry for Physical Sciences" (in session "Learning from Complex Data")

Lecture "NFDI4Chem" (in session "Open Data")

Lecture "Science in the Age of Machine Learning" (in session "Machine Learning - Theory")

Lecture "Structuring, analyzing, and harvesting big data in materials science electron microscopy" (in session "Open Data")

Lecture "Variational methods in material sciences" (in session "Multidimensional Data Analysis")

Multidimensional Photoemission Spectroscopy: proposal & demonstration of data (infra)structure (in session "Poster Session: Machine Learning -Theory & Applications / Open Data")

Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning (in session "Poster Session: Machine Learning -Theory & Applications / Open Data")

Teaching solid mechanics to artificial intelligence (in session "Poster Session: Machine Learning -Theory & Applications / Open Data")

Tutorial "Active Learning with Bayesian Optimization" (in session "Machine Learning - Theory")

Tutorial "Compressed sensing meets symbolic regression: learning interpretable models" (in session "Machine Learning - Theory")

Tutorial "Introduction to EELS analysis with HyperSpy" (in session "Multidimensional Data Analysis")

Tutorial "Neural Networks with Euclidean Symmetry for Physical Sciences" (in session "Learning from Complex Data")

Tutorial "Neural Networks" (in session "Machine Learning - Theory & Application")

Tutorial "Sharing, publishing, and managing computational materials science data with NOMAD" (in session "Open Data")

Tutorial "Tips, tricks, and tools for reproducible materials science" (in session "Learning from Complex Data")

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Events scheduled on*

13/09/2021

14/09/2021

15/09/2021

16/09/2021

17/09/2021

Include materials from sessions/contributions scheduled on the selected dates