Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties.

ABSTRACT: Metal-organic frameworks (MOFs) promise to engender technology-enabling properties for numerous applications. However, one significant challenge in MOF development is their overwhelmingly large design space, which is intractable to fully explore even computationally. To find diverse optimal MOF designs without exploring the full design space, we develop Vendi Bayesian optimization (VBO), a new algorithm that combines traditional Bayesian optimization with the Vendi score, a recently introduced interpretable diversity measure. Both Bayesian optimization and the Vendi score require a kernel similarity function, we therefore also introduce a novel similarity function in the space of MOFs that accounts for both chemical and structural features. This new similarity metric enables VBO to find op

SUBMITTER: Liu TW 

PROVIDER: S-EPMC11499977 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature