Strasser Nina, Wieser Sandro, Zojer Egbert
Institute of Solid State Physics, NAWI Graz, Graz University of Technology, 8010 Graz, Austria.
Int J Mol Sci. 2024 Mar 5;25(5):3023. doi: 10.3390/ijms25053023.
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 and 7 cm. 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.
本研究聚焦于HKUST-1的自旋相关振动特性，HKUST-1是一种在气体存储和分离方面具有潜在应用价值的金属有机框架材料。采用密度泛函理论（DFT），我们探究了铜桨轮（作为HKUST-1的二级结构单元）中的自旋耦合对材料振动特性的影响。通过系统地筛选自旋态对不同频率区域的声子能带和态密度的影响，我们确定不对称的 -COO- 伸缩振动受不同类型磁耦合的影响最大。值得注意的是，我们还表明，使用适当参数化的、最先进的机器学习经典势可以定量再现DFT得出的见解，与DFT结果的均方根偏差在3 cm至7 cm之间。这证明了机器学习经典力场在预测复杂材料自旋相关特性方面的潜力，即使在力场参数化过程中仅为生成参考数据而明确考虑自旋的情况下也是如此。
