Simulations of High Temperature Decomposition of Metal-Organic Frameworks to form Amorphous Catalysts

TL;DR

This study uses machine learning-based simulations to explore how high-temperature decomposition of metal-organic frameworks leads to amorphous catalysts, providing insights into structure-property relationships and guiding catalyst design.

Contribution

It demonstrates the effectiveness of neural network interatomic potentials in modeling MOF pyrolysis under realistic conditions, revealing atomistic mechanisms of catalyst formation.

Findings

MLIPs accurately model high-temperature MOF dynamics.

Copper doping influences structural evolution during pyrolysis.

Simulations align with experimental observations of gas release and nanoparticle formation.

Abstract

Metal-organic framework (MOF) derived materials formed through high temperature processes show great potential as catalysts. However, understanding of structure-property relationships between the initial MOF and the resulting MOF-derived catalyst is limited because the amorphous nature of the catalyst challenges standard structural characterization methods. Neural network approaches that learn interatomic potentials from density functional theory offer a promising solution. We simulated the pyrolysis of UiO-66, UiO-67 and MIP-206 using both foundational and fine-tuned machine learned interatomic potentials (MLIPs). To mimic experimental conditions, an atmosphere of CO2 and H2 was introduced and the structures were doped with 20 wt% copper to probe the effect of copper on the structural evolution of MOFs. These simulations provide atomistic insights into gas evolution, metal nanoparticle formation, and linker decomposition that were compared to available experimental data. Overall, this work demonstrates the potential of MLIPs to accurately model high temperature MOF dynamics under experimentally relevant conditions and guide the design of new catalytic materials.