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

Connor W. Edwards, Oliver M. Linder-Patton, Jack D. Evans

TL;DR

The paper addresses the challenge of deciphering structure–property relationships in MOF-derived catalysts formed by high-temperature pyrolysis. It implements a workflow where foundational MLIPs are evaluated against high-temperature DFT data and then fine-tuned (with PT data) to produce reliable nanosecond MOF pyrolysis trajectories under catalytically relevant CO2/H2 conditions with Cu doping. The study shows that unmodified foundational models yield unphysical dynamics, while fine-tuned models capture copper-enhanced linker decomposition, formation of extended amorphous carbon sheets, and dispersion of copper within zirconium oxide domains, enabling atomistic insights into catalyst formation. These results demonstrate the viability of MLIPs for simulating MOF chemistry at industrially relevant conditions, while also highlighting the need for larger system sizes to obtain quantitative nanoparticle morphology predictions for catalyst design.

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.

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

TL;DR

The paper addresses the challenge of deciphering structure–property relationships in MOF-derived catalysts formed by high-temperature pyrolysis. It implements a workflow where foundational MLIPs are evaluated against high-temperature DFT data and then fine-tuned (with PT data) to produce reliable nanosecond MOF pyrolysis trajectories under catalytically relevant CO2/H2 conditions with Cu doping. The study shows that unmodified foundational models yield unphysical dynamics, while fine-tuned models capture copper-enhanced linker decomposition, formation of extended amorphous carbon sheets, and dispersion of copper within zirconium oxide domains, enabling atomistic insights into catalyst formation. These results demonstrate the viability of MLIPs for simulating MOF chemistry at industrially relevant conditions, while also highlighting the need for larger system sizes to obtain quantitative nanoparticle morphology predictions for catalyst design.

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.
Paper Structure (7 sections, 4 figures, 3 tables)

This paper contains 7 sections, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Computational Methods
  3. Results and Discussion
  4. Validating foundational models
  5. Fine-tuning foundational models
  6. Analysis of fine-tuned 1 ns quench simulations
  7. Conclusion and Outlook

Figures (4)

  • Figure 1: MOF structures of a) MIP-206, b) UiO-67 and c) UiO-66 with a 20 wt% copper loading and a 40 bar atmosphere of CO2 and H2 in a 1:3 ratio. Zirconium atoms are shown in teal, oxygen in red, hydrogen in pink, carbon in black and copper in dark orange. d) An example of the temperature profile for the 1 ns quenches conducted at 2000 K.
  • Figure 2: Linker decomposition analysis indicating the sequential loss of one and both carboxylate groups in (a) MIP-206, (b) UiO-66, and (c) UiO-67. (d) Schematic showing decomposition products of the UiO-66 linker.
  • Figure 3: Example structures of amorphous carbon and metal nanoparticles extracted from the final structure of 1 ns quenches of MIP-206, UiO-66, and UiO-67 at 0 and 20 wt% copper loadings. Zirconium atoms are shown in teal, oxygen in red, hydrogen in pink, carbon in black and copper in dark orange.
  • Figure 4: Example structures of amorphous carbon and metal nanoparticles extracted from the final structure of 1 ns quenches of a $2\times2\times2$ UiO-66 simulation at 0 and 20 wt% copper loadings. Zirconium atoms are shown in teal, oxygen in red, hydrogen in pink, carbon in black and copper in dark orange.