Hunting Structural Demons in Digital Reticular Chemistry: Lessons from Metal-Organic Frameworks

TL;DR

This paper discusses the importance of detecting and preventing structural errors, termed 'structural demons,' in digital reticular chemistry, especially in metal-organic frameworks, to improve computational screening accuracy.

Contribution

It identifies sources of structural errors in MOF databases and proposes strategies for their detection and prevention to enhance data reliability.

Findings

Over half of top candidates in screening are chemically invalid.

Structural errors originate from experimental data conversion and hypothetical structure generation.

Preventative measures include better data curation and filtering before structure generation.

Abstract

Digital reticular chemistry relies on accurate crystal structures to power computational screening, data-driven discovery, and structure-property analysis, yet recent studies reveal that more than half of the top-performing candidates in major computational screening campaigns are chemically invalid. In experimental MOF databases, structural errors arise when disordered or incomplete structural models are incorrectly converted into fully specified simulation inputs. In hypothetical MOF database, structures are complete by construction but may encode chemically implausible oxidation states, coordination environments, or charge distributions. We term these erroneous structural models "structural demons." This mini-review asks three questions: where these errors enter, how we find them, and how we prevent them. On the prevention side, the key steps are keeping diffraction data and synthesis details together from the start, using consistent curation when structures enter a database, and filtering topology choices before structure generation. Connecting these steps can keep many bad structures out of downstream databases and reduce the need to fix them later.