Work

The CHGNet uMLIP model fine-tuned for 2Hc-WS2

Year: 2026

Type: dataset

Abstract: The CHGNet uMLIP model fine-tuned for 2Hc-WS2 in P. Žguns, I. Pudza, A. Kuzmin, Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2, J. Chem. Theory Comput. 21 (2025) 8142–8150. Doi: 10.1021/acs.jctc.5c00955

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22059539

Author: Žguns, Pjotrs

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7204010083

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Cited by: 1

Related to: 10