Dresden 2020 – wissenschaftliches Programm
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SYBD 1: Big Data Driven Materials Science
Dienstag, 17. März 2020, 09:30–12:15, HSZ 02
Combining concepts from big data analytics with experimental and theoretical techniques in solid state physics has opened exciting new routes to designing materials with superior mechanical, electronic or optical properties as well as to enhance resolution and performance of established experimental techniques as e.g. electron microscopy, x-ray diffraction, or atom probe tomography. The symposium will bring together leading experts who pioneer the application of these techniques for their respective fields. The intention is to show success stories but also to critically discuss present limitations as well as emerging areas. A critical aspect that will be in the focus of the symposium is that big data analytics alone, i.e. without a deep understanding of the underlying physics, turns out to be insufficient in successfully addressing experiment or materials related challenges.
Topics to be addressed in the symposium are: exploring high-dimensional chemical, crystallographic and microstructural compound spaces by big data analytics; linking physical, chemical, and mechanical theories with materials data platforms across scales; pushing resolution limits of atomic-scale and meso-scale experimental techniques in microscopy, spectroscopy, and tomography; applications ranging from structural materials surviving extreme conditions to soft matter and solid-state surfaces.
|09:30||SYBD 1.1||Hauptvortrag: Materials innovation driven by data and knowledge systems — •Surya Kalidindi|
|10:00||SYBD 1.2||Hauptvortrag: Network Theory Meets Materials Science — •Chris Wolverton, Murat Aykol, and Vinay Hegde|
|10:30||SYBD 1.3||Hauptvortrag: Verification and error estimates for ab initio data — •Claudia Draxl|
|11:00||15 min. break|
|11:15||SYBD 1.4||Hauptvortrag: Identifying Domains of Applicability of Machine Learning Models for Materials Science — •Mario Boley, Christopher Sutton, Luca M. Ghiringhelli, Matthias Rupp, Jilles Vreeken, and Matthias Scheffler|
|11:45||SYBD 1.5||Hauptvortrag: Deep learning of low-dimensional latent space molecular simulators — •Andrew Ferguson|