Investigating the atomic-level mechanisms of negative thermal expansion in Zn(CN)2 using machine learning-enabled molecular dynamics

GY Shi and ZC Zhong and YH Su and R He and C Zhang, PHYSICA SCRIPTA, 100, 066002 (2025).

DOI: 10.1088/1402-4896/add399

The negative thermal expansion behavior (NTE) of Zn(CN)2 over a wide range of temperature has received significant attention. Here, we develop a machine learning interatomic potential and perform large-scale molecular dynamics simulations through which we unveil the NTE mechanisms of Zn(CN)2. Analysis of the trajectories of atoms reveal that the C and N atoms move on the lateral side of Zn & mldr;Zn axis with different distance. The C equivalent to N bond is not exactly parallel to the Zn & mldr;Zn axis. The bridging C and N atoms exhibit a unique motion behavior that combines lateral movement with a tilted aspect, which we term the 'lateral + tilt' mode. The transition pressure from cubic to orthorhombic phase under ambient temperature is found to be 2.7 GPa. Machine learning-enabled molecular dynamics open a way to better comprehend the physical origins of NTE.

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