Ran Mo^1*, Zhishuo Huang^1*, Liviu Ungur^1*

1 Department of Chemistry, National University of Singapore, Block S8 Level 3, 3 Science Drive 3, 117543, Singapore

* Corresponding authors emails: e0261925@u.nus.edu, zhishuohuang@gmail.com, chmlu@nus.edu.sg

DOI10.24435/materialscloud:qq-w0 [version v1]

Publication date: Apr 11, 2025

How to cite this record

Ran Mo, Zhishuo Huang, Liviu Ungur, Efficient modeling of dynamic properties in K₃C₆₀ using machine learning force fields, Materials Cloud Archive 2025.58 (2025), https://doi.org/10.24435/materialscloud:qq-w0

Description

Fullerides forms a big familiy of molecular crystals exhibiting various useful electro- and magnetochemical properties. Therefore, an efficient in silico method is desirable to look into such chemical system. To alleviate the burden of computationally-heavy first-principles calculations, here we present the successful attempts of using Machine Learning Force Field (MLFF) in predicting dynamical properties of the alkali-doped fulleride K₃C₆₀. Two on-the-fly Gaussian-Process-MLFF schemes based on different atomistic descriptors, Smooth Overlap of Atomic Position (SOAP) and Atomic Cluster Expansion (ACE), have been experimented. The performance of generated K₃C₆₀ MLFFs are validated by accurate prediction on energy and forces of 1,000 randomly disturbed K₃C₆₀ structures compared to respective DFT results. Several other dynamical properties including phonon dispersion, elastic moduli and heat capacity obtained by MLFFs have also shown good agreement with results from DFT calculations, whose computational cost on such post-processing is several times more expensive than training a single MLFF that performs effortless post-processing. This shows the potential of applying MLFF to complex molecular crystal systems and pave the way to the investigation of much more intricate fulleride properties.

Materials Cloud sections using this data

Files

File name	Size	Description
MLFF.tar.gz MD5md5:67d9ca316e5e8c551d7c2eba2364e665	248.9 MiB	Necessary input and output files for reproducing MLFF model and related validations.
README MD5md5:000704cd6a1149fd081c63d03e6c0c03	1.5 KiB	README file

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External references

Keywords

Machine learning force field Molecular crystal Dynamical properties