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

Area of Science:

• Materials Science
• Computational Chemistry
• Condensed Matter Physics

Background:

• HKUST-1 is a metal-organic framework with significant potential in gas storage and separation.
• Understanding its spin-dependent vibrational properties is crucial for optimizing its performance.
• Copper paddle wheels are key building units influencing HKUST-1's characteristics.

Purpose of the Study:

• To investigate the impact of spin couplings within HKUST-1's copper paddle wheels on its vibrational properties.
• To identify which vibrational modes are most sensitive to magnetic interactions.
• To assess the feasibility of using machine-learned potentials for predicting these spin-dependent properties.

Main Methods:

• Density Functional Theory (DFT) calculations were employed to simulate spin-dependent vibrational properties.
• Phonon band structures and densities of states were systematically analyzed under varying spin conditions.
• Machine-learned classical potentials were developed and validated against DFT results.

Main Results:

• Asymmetric -COO- stretching vibrations were found to be most affected by different magnetic couplings.
• DFT calculations revealed distinct changes in phonon bands and densities of states due to spin states.
• Machine-learned potentials reproduced DFT findings with high accuracy (3-7 cm^-1 RMSD).

Conclusions:

• Spin interactions play a critical role in determining the vibrational behavior of HKUST-1.
• Machine-learned force fields offer a computationally efficient and accurate method for predicting spin-dependent material properties.
• This work paves the way for designing advanced materials with tailored spin-vibrational responses.