Computational Design of Metal–Organic Frameworks with Unprecedented High Hydrogen Working Capacity and High Synthesizability

1Korea Advanced Institue of Science and Technology, 2LG Chem

Abstract

Compared to conventional computational screening studies that are limited by the size of database, inverse design has a great potential to facilitate identifying new materials with optimal properties. In this work, we integrate machine learning with genetic algorithm to computationally design metal–organic frameworks (MOFs) for hydrogen storage applications at cryogenic conditions. As such, we identified 6277 MOFs that exceed the current record (37.2 g/L of NPF-200) at operating conditions between 5 and 100 bar at 77 K. MOFs, whose working capacities exceed 40.0 g/L (system-based 2025 DOE target) were also identified, where the highest working capacity obtained from this work was 41.6 g/L, which is higher than any other hypothetical MOFs reported thus far. Furthermore, synthesizability of the top performing structures was assessed by comparing relative stability with their polymorphic structures while taking into account the possibility of interpenetration. We demonstrate that our methodology can successfully design MOFs with both high hydrogen capacity and synthesizability and we anticipate our workflow can be widely applied to various other materials and applications.

Genetic Algorithm Workflow

Proposed Hypothetical MOF structures

The genetic algorithm successfully generated MOF structures with high hydrogen working capacities. We further applied synthesizability metric, which assesses the sythesizabiltiy of a given structure through energy comparison with polymorph structures. As a result, we proposed the following structures which exhibit exceptional hydrogen adsorption behavior with high synthesizability at the same time.