Data-driven methodologies are now central to materials science, but their impact depends critically on effective data sharing. FAIR data management and open science enable data reuse, verification, and systematic exploitation across the vast chemical space of materials. While these approaches have been highly successful for ground-state materials discovery based on Density Functional Theory (DFT), similar progress has not yet been achieved for more advanced simulation frameworks.

Theoretical spectroscopy and Green’s function methods, such as GW, TDDFT, BSE, DMFT, and KKR, are essential for describing excited-state properties and electronic correlations, yet they pose major FAIR data challenges. These simulations are computationally intensive, generate complex datasets, and rely on heterogeneous software ecosystems. Despite recent database efforts, limited interoperability and incomplete workflow provenance continue to hinder reproducibility and reuse.

Within the FAIRmat initiative, the NOMAD platform now supports theoretical spectroscopy and Green’s function simulations, hosting large numbers of GW, BSE, and DMFT workflows with full provenance. The objective of this workshop is to address the remaining interoperability challenge by defining common data structures and workflows, enabling consistent data exchange, reproducibility, and large-scale reuse. Achieving this goal requires close collaboration between method developers, materials scientists, data scientists, and data management experts.

Are you interested in presenting a contributed talk or poster? Please provide a title, short description, and preferred format upon application.