MOFlar

MOFlar is a modular open-source software framework designed to support the modeling, simulation, and analysis of metal-organic frameworks (MOFs) and related porous materials. It is intended as a standardized, extensible platform to facilitate data sharing, reproducible workflows, and rapid prototyping of MOF-based materials for gas storage, separations, and catalysis.

Its architecture centers on a graph-based representation of MOF structures, a workflow engine, and a plug-in ecosystem. Core components include a MOF graph model that captures nodes as metal clusters and organic linkers, a metadata layer for synthetic procedures, and adapters for common simulation tools. The project supports standard data formats such as CIF for structures and JSON-LD for metadata, with facilities for provenance tracking and containerized execution to ensure reproducibility. Users can create, save, and share workflows that sequence structure optimization, property calculations, and ML-based predictions.

Key capabilities include high-throughput screening of MOFs for gas adsorption and selectivity, machine-learning guided property prediction, and visualization of pore topology and accessibility. MOFlar emphasizes interoperability, enabling researchers to compare results across different software packages and to integrate experimental data.

Development and governance are community-driven, with an open-source license, public issue trackers, and contributor guidelines. Documentation covers installation, data schemas, workflow design, and tutorials. The project aims to complement existing MOF databases and informatics efforts by providing a common interface for data exchange and method interoperability.

See also: Metal-organic framework, MOF databases, Computational materials science, High-throughput screening.