BiGmax Workshop 2021

Session

Session IV

15 Apr 2021, 14:00

Virtually

58. Knowledge-Based Approaches in Catalysis and Energy Modelling (30 min talk + 15 min discussion)

Karsten Reuter (invited) (FHI Berlin)

15/04/2021, 14:00

Talk

Data sciences are now also entering theoretical catalysis and energy related research with full might. Automatized workflows and the training of machine learning approaches with first-principles data generate predictive-quality insight into elementary processes and process energetics at undreamed-of pace. Computational screening and data mining allows to explore these data bases for promising...

37. NOMAD – A FAIR Data Sharing Platform for Materials Science (12 min talk + 3 min discussion)

Markus Scheidgen (Humboldt Universität zu Berlin / Fritz Haber Institut der Max Planck Gesellschaft)

15/04/2021, 14:45

Talk

As an integral part of the FAIR-DI/FAIRmat initiatives, NOMAD is extending it's scope. NOMAD evolves from a central repository for publishing electronic structure codes data into a federated data management network that covers all branches of materials science. Instead of just using NOMAD to publish final results, we want to show how on-site installations of NOMAD can help to manage your local...

56. On Software Tools Which Assist Electron Microscopists with Sharing Metadata and Numerical Results in Accordance with the FAIR Data Stewardship Principles (12 min talk + 3 min discussion)

Markus Kühbach

15/04/2021, 15:00

Talk

Microscopy and spectroscopy experiments and the associated computational and theoretical analyses of data from such experiments are the resources of laboratory and data-processing workflows that yield numerical data and contextualization through metadata. The purpose of such experiments is ideally accurate and precise delivery of quantitative evidence in support of or against a formulated set...

38. Approaches to FAIR TEM data (12 min talk + 3 min discussion)

Niels Cautaerts (Max-Planck-Institut für Eisenforschung)

15/04/2021, 15:15

Talk

Transmission electron microscopy data is rich in quantitative information about materials, information that could in theory be coupled to atomistic simulations, but extracting and harnessing that information is non-trivial. Machine learning approaches may facilitate this, but these are hampered by the limited availability and interoperability of the data. In this talk we present approaches,...