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Integrating physically-based modeling techniques into ML methods may lead to better performance. In a recent work by Chew et al. (\u201c<jats:italic>Advancing material property prediction: using physics-informed machine learning models for viscosity<\/jats:italic>\u201d) descriptors from classical molecular dynamics (MD) simulations were included into a quantitative structure\u2013property relationship to accurately predict temperature-dependent viscosity of pure liquids. Through feature importance analysis, the authors found that heat of vaporization was the most relevant descriptor for the prediction of viscosity. In this comment, we would like to discuss the physical origin of this finding by referring to Eyring\u2019s rate theory, and develop an alternative modeling approach using a thermodynamic-based architecture that requires less input data.<\/jats:p>","DOI":"10.1186\/s13321-025-01070-9","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T10:47:20Z","timestamp":1756378040000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Comment on \u201cAdvancing material property prediction: using physics-informed machine learning models for viscosity\u201d"],"prefix":"10.1186","volume":"17","author":[{"given":"Maximilian","family":"Fleck","sequence":"first","affiliation":[]},{"given":"Samir","family":"Darouich","sequence":"additional","affiliation":[]},{"given":"Marcelle B. 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