MOFBuilder: automated end-to-end modeling of MOF dynamics for high-throughput screening
Source abstract
Abstract
Abstract The vast chemical design space of Metal-Organic Frameworks (MOFs) offers unparalleled opportunities for targeted materials design, yet computational screening remains largely restricted to static structure derived from the CIF file. We introduce MOFBuilder, a modular end-to-end pipeline that leverages molecular-level identities to automatically generate chemically consistent, molecular dynamics (MD) ready MOF models, flexibly supporting periodic, defective, cluster, and slab representations. By eliminating the manual effort typically required for model preparation, the pipeline enables a seamless construction of complex systems ranging from large-scale bio-hybrid interfaces to functionalized high-throughput libraries. As proof of the need for high-throughput dynamic modeling, we show that dynamic screening is necessary to circumvent the “Porosity Paradox". Several functionalized UiO-66 variants classified as non-porous by static geometric analysis exhibit significant CO 2 uptake through gate-opening mechanisms captured only via MD. By enabling the high-throughput generation of consistent, dynamic datasets, MOFBuilder addresses a critical gap in discovery pipelines and provides the foundation for more predictive, data-driven materials design.
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Research context
OpenAlex topics: Metal-Organic Frameworks: Synthesis and Applications · Machine Learning in Materials Science · X-ray Diffraction in Crystallography
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (77)
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