Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks

Castel, Nicolas; André, Dune; Edwards, Connor; Evans, Jack D.; Coudert, François-Xavier

doi:10.1039/d3dd00236e

Cited by 2 publications

(2 citation statements)

References 71 publications

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“…A possible strategy for massively speeding up simulations dealing with quantities related to the structural dynamics of materials is the use of machine-learned potentials (MLPs) [ 27 , 28 , 29 , 30 ], which have also been applied to the modelling of MOFs [ 31 , 32 ]. Additionally, MLPs have been applied to successfully predict phonon properties.…”

Section: Introductionmentioning

confidence: 99%

Predicting Spin-Dependent Phonon Band Structures of HKUST-1 Using Density Functional Theory and Machine-Learned Interatomic Potentials

Strasser,

Wieser,

Zojer

2024

IJMS

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The present study focuses on the spin-dependent vibrational properties of HKUST-1, a metal–organic framework with potential applications in gas storage and separation. Employing density functional theory (DFT), we explore the consequences of spin couplings in the copper paddle wheels (as the secondary building units of HKUST-1) on the material’s vibrational properties. By systematically screening the impact of the spin state on the phonon bands and densities of states in the various frequency regions, we identify asymmetric -COO- stretching vibrations as being most affected by different types of magnetic couplings. Notably, we also show that the DFT-derived insights can be quantitatively reproduced employing suitably parametrized, state-of-the-art machine-learned classical potentials with root-mean-square deviations from the DFT results between 3 cm−1 and 7 cm−1. This demonstrates the potential of machine-learned classical force fields for predicting the spin-dependent properties of complex materials, even when explicitly considering spins only for the generation of the reference data used in the force-field parametrization process.

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Section: Introductionmentioning

confidence: 99%

Predicting Spin-Dependent Phonon Band Structures of HKUST-1 Using Density Functional Theory and Machine-Learned Interatomic Potentials

Strasser,

Wieser,

Zojer

2024

IJMS

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“…In this work, we used this new MLFF to investigate the fracture behavior of different ZIF glasses. We note that two other MLFFs have recently been reported, , but they are not parametrized for ZIF systems with different organic linkers.…”

Section: Introductionmentioning

confidence: 99%

Structural Origin of the Deformation Propensity of Zeolitic Imidazolate Framework Glasses

Du,

Ge,

Cao

et al. 2024

Chem. Mater.

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Zeolitic imidazolate framework (ZIF) glasses featuring nanoscale porosity have attracted significant attention due to their potential applications in catalysis, energy storage, gas sorption, and separation. However, their mechanical properties may limit some of these applications. In this work, we investigate the structural origins of the variation of mechanical properties of zinc-based ZIF-62 (ZnIm2–x bIm x ) glasses with different benzimidazolate (bIm) to imidazolate (Im) ratios. This is achieved using large-scale molecular dynamics simulations with a recent machine learning force field. We find that the simulated ZIF glass structures match those determined using X-ray total scattering. The relatively large bIm group is found to hinder the reconstruction of the coordination network during quenching of the ZIF melts, leading to more disordered Zn tetrahedra. Both Young’s modulus and fracture toughness decrease with an increase in bIm content. Upon fracturing, all of the organic linkers remain intact, while Zn–N bond switching dissipates the strain energy. By correlating the atomic dynamics with the static structure, we find that the deformation propensity of ZIF glass is correlated with Zn mobility, which is in turn determined by the initial atomic volume before deformation across a variety of glass compositions and strain values. These findings could be helpful for designing more fracture-resistant metal–organic framework glasses in the future.

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Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks

Abstract: The detailed understanding of the microscopic structure of amorphous phases of metal-organic frameworks (MOFs) remains a widely open question: characterization of these systems is very difficult, both from the experimental...

Cited by 2 publications

References 71 publications

Predicting Spin-Dependent Phonon Band Structures of HKUST-1 Using Density Functional Theory and Machine-Learned Interatomic Potentials

Predicting Spin-Dependent Phonon Band Structures of HKUST-1 Using Density Functional Theory and Machine-Learned Interatomic Potentials

Structural Origin of the Deformation Propensity of Zeolitic Imidazolate Framework Glasses

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