Hardening Computational Materials-Science Workflows against Human Errors (A03)

• Subject Area: Theoretical Condensed Matter Physics ; Software Engineering and Programming Languages
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 414984028

Ab-initio codes of Computational Materials Science (CMS) can predict a variety of materials properties. However, scientists are confronted with a huge variability of such workflows, which can lead to user interaction errors. In phase I, it was focused on improving this situation for individual tasks the project within a CMS DAW by developing systematic testing strategies. In the second phase, we will extend our focus to address the complete CMS data analysis pipeline including downstream machine learning applications. The project will research new technologies for creating guide-lines and recommendations for scientists about how to compute material properties with a desired level of accuracy.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1404: FONDA - Foundations of Workflows for Large-Scale Scientific Data Analysis
• Applicant Institution: Humboldt-Universität zu Berlin
• Project Heads: Professorin Dr. Claudia Draxl; Professor Dr. Lars Grunske; Dr. Pasquale Pavone