End-To-End Discovery of MOFs for Ambient CH4 Adsorption.

Abstract

We report an end-to-end computational-experimental workflow for the discovery of metal-organic frameworks (MOFs), demonstrated by the computational design and synthesis of two novel Zn-based frameworks, UCHI-1 and UCHI-2, exhibiting enhanced methane uptake and selectivity at low pressure under ambient conditions (298 K, 1 bar). The workflow enables the rational selection and experimental realization of metal-organic frameworks combining data mining, machine-learning driven adsorption prediction, and structure generation, with experimental synthesis and validation within a closed-loop discovery pipeline. Analysis of existing and newly generated MOFs reveals the structure-property relationships governing low-pressure methane adsorption, identifying an optimal pore size and shape, framework densities, linker functionalities, and framework topologies that maximize dispersive C-H/π and van der Waals interactions. Beyond the specific materials identified herein, the results establish this workflow as a scalable and extensible platform for accelerated MOF discovery, with clear routes toward further optimization and automation while demonstrating practical applicability beyond purely theoretical exploration of hypothetical materials.