Prof. Dr. Claudia Draxl
Deriving Trust Levels for Multi-Choice Data Analysis Workflows
Humboldt-Universität zu Berlin
HPC Platform used:
NHR@Göttingen, NHR@ZIB: HLRN Clusters Lise and Emmy
Bringing data from various sources together, poses severe challenges to their interoperability. A prerequisite to using such data together, e.g. in machine-learning tasks, requires the assessment of the data quality. The project described here, aims at doing so by deriving trust levels for data from density-functional theory (DFT). A trust level shall be assigned for a material based on what approximation (density functional) and what numerical settings were used in the DFT simulation.