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000637337 0247_ $$2ISSN$$a1549-9626
000637337 0247_ $$2datacite_doi$$a10.3204/PUBDB-2025-03829
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000637337 1001_ $$0P:(DE-H253)PIP1114016$$aZguns, Pjotrs$$b0$$eCorresponding author
000637337 245__ $$aBenchmarking CHGNet Universal Machine Learning Interatomic Potential against DFT and EXAFS: The Case of Layered WS$_2$ and MoS$_2$
000637337 260__ $$aWashington, DC$$b[Verlag nicht ermittelbar]$$c2025
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000637337 520__ $$aUniversal machine learning interatomic potentials (uMLIPs) deliver near \emph{ab initio} accuracy in energy and force calculations at a low computational cost, making them invaluable for materials modeling. Although uMLIPs are pretrained on vast \emph{ab initio} data sets, rigorous validation remains essential for their ongoing adoption. In this study, we use the CHGNet uMLIP to model thermal disorder in isostructural layered 2H$_c$-WS$_2$  and 2H$_c$-MoS$_2$, benchmarking it against \emph{ab initio} data and extended X-ray absorption fine structure (EXAFS) spectra, which capture thermal variations in bond lengths and angles. Fine-tuning CHGNet with compound-specific \emph{ab initio} (density functional theory (DFT)) data mitigates the systematic softening (i.e., force underestimation) typical of uMLIPs and simultaneously improves the alignment between molecular dynamics-derived and experimental EXAFS spectra. While  fine-tuning with a single DFT structure is viable, using $\sim$100 structures is recommended to accurately reproduce EXAFS spectra and achieve DFT-level accuracy. Benchmarking the CHGNet uMLIP against both DFT and experimental EXAFS data reinforces confidence in its performance and provides guidance for determining optimal fine-tuning data set sizes.
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000637337 536__ $$0G:(EU-Grant)730872$$aCALIPSOplus - Convenient Access to Light Sources Open to Innovation, Science and to the World (730872)$$c730872$$fH2020-INFRAIA-2016-1$$x2
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000637337 7001_ $$0P:(DE-H253)PIP1029767$$aPudza, Inga$$b1
000637337 7001_ $$0P:(DE-H253)PIP1009042$$aKuzmin, Aleksejs$$b2$$eCorresponding author
000637337 773__ $$0PERI:(DE-600)2166976-4$$a10.1021/acs.jctc.5c00955$$gVol. 21, no. 16, p. 8142 - 8150$$n16$$p8142 - 8150$$tJournal of chemical theory and computation$$v21$$x1549-9618$$y2025
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