A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models

TL;DR

This study creates a large database of ultrastable metal-organic frameworks (MOFs) by recombining stable fragments using machine learning, enabling the discovery of thermally and mechanically robust MOFs for gas storage.

Contribution

We developed a novel approach to generate a vast database of ultrastable MOFs by fragment recombination guided by machine learning, surpassing previous databases in stability and connectivity.

Findings

Order of magnitude more ultrastable MOFs identified

Ultrastable MOFs are more thermally stable than average

Computed mechanical properties confirm good stability

Abstract

High-throughput screening of large hypothetical databases of metal-organic frameworks (MOFs) can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning (ML) models to identify MOFs that are thermally stable and stable upon activation. We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50,000 structures that samples orders of magnitude more connectivity nets and inorganic building blocks than prior databases. This database shows an order of magnitude enrichment of ultrastable MOF structures that are stable upon activation and more than one standard deviation more thermally stable than the average experimentally characterized MOF. For the nearly 10,000 ultrastable MOFs, we compute bulk elastic moduli to confirm these materials have good mechanical stability, and we report methane deliverable capacities. Our work identifies privileged metal nodes in ultrastable MOFs that optimize gas storage and mechanical stability simultaneously.