Comment on "Advancing material property prediction: using physics-informed machine learning models for viscosity".

Maximilian Fleck, Samir Darouich, Marcelle B M Spera, Niels Hansen

Abstract readLetter

In one paragraph

Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Maximilian FleckInstitute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569, Stuttgart, Germany.

Samir DarouichInstitute for Artificial Intelligence, University of Stuttgart, Universitätsstraße 32, 70569, Stuttgart, Germany.

Marcelle B M SperaInstitute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569, Stuttgart, Germany.

Niels HansenInstitute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569, Stuttgart, Germany. hansen@itt.uni-stuttgart.de.

Funding

Deutsche Forschungsgemeinschaft EXC 2075 - 390740016

6 · The paper itself

Abstract

When data availability is limited, the prediction of properties through purely data-driven machine learning (ML) is challenging. Integrating physically-based modeling techniques into ML methods may lead to better performance. In a recent work by Chew et al. ("Advancing material property prediction: using physics-informed machine learning models for viscosity") descriptors from classical molecular dynamics (MD) simulations were included into a quantitative structure-property 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's rate theory, and develop an alternative modeling approach using a thermodynamic-based architecture that requires less input data.

Indexed as

Eyring rate theoryPhysics-inspired neural networkViscosity

Identifiers

PMID40877993

DOI10.1186/s13321-025-01070-9

PMCPMC12392590

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.