Journal Article

PUBDB-2025-03829

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Benchmarking CHGNet Universal Machine Learning Interatomic Potential against DFT and EXAFS: The Case of Layered WS$_2$ and MoS$_2$

Zguns, P. (Corresponding author)Extern* ; Pudza, I.Extern* ; Kuzmin, A. (Corresponding author)Extern*

2025
[Verlag nicht ermittelbar] Washington, DC

This record in other databases:    

Please use a persistent id in citations: doi:10.1021/acs.jctc.5c00955  doi:10.3204/PUBDB-2025-03829

Abstract: Universal machine learning interatomic potentials (uMLIPs) deliver near \emph{ab initio} accuracy in energy and force calculations at a low computational cost, making them invaluable for materials modeling. Although uMLIPs are pretrained on vast \emph{ab initio} data sets, rigorous validation remains essential for their ongoing adoption. In this study, we use the CHGNet uMLIP to model thermal disorder in isostructural layered 2H$_c$-WS$_2$ and 2H$_c$-MoS$_2$, benchmarking it against \emph{ab initio} data and extended X-ray absorption fine structure (EXAFS) spectra, which capture thermal variations in bond lengths and angles. Fine-tuning CHGNet with compound-specific \emph{ab initio} (density functional theory (DFT)) data mitigates the systematic softening (i.e., force underestimation) typical of uMLIPs and simultaneously improves the alignment between molecular dynamics-derived and experimental EXAFS spectra. While fine-tuning with a single DFT structure is viable, using $\sim$100 structures is recommended to accurately reproduce EXAFS spectra and achieve DFT-level accuracy. Benchmarking the CHGNet uMLIP against both DFT and experimental EXAFS data reinforces confidence in its performance and provides guidance for determining optimal fine-tuning data set sizes.

---

Contributing Institute(s):

1. DOOR-User (DOOR ; HAS-User)

Research Program(s):

1. 6G3 - PETRA III (DESY) (POF4-6G3) (POF4-6G3)
2. FS-Proposal: I-20170739 EC (I-20170739-EC) (I-20170739-EC)
3. CALIPSOplus - Convenient Access to Light Sources Open to Innovation, Science and to the World (730872) (730872)

Experiment(s):

1. PETRA Beamline P65 (PETRA III)

Appears in the scientific report 2025

---

Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

---