liuganghuggingface/moldata-chemixhub_logv

CheMixHub logV

CheMixHub data for log-transformed mixture viscosity, with component molecular structures, mixture composition, source conditions, and property labels. Each label describes the complete mixture.

Part of the Molecular Datasets collection.

Get started

from datasets import load_dataset
dataset = load_dataset("liuganghuggingface/moldata-chemixhub_logv", "000_data")

Where the data comes from

• Original data: published with the original study
• Redistributed by: CheMixHub (dataset page)

Tables

Config	Splits	Rows	Targets
000_data	full	34,374	value (regression; units not specified)

data/ contains rows with readable molecular structures; identical observations are collapsed. observations/ contains all source rows.

Source label columns are text, including values such as <10. SMILES_original retains the input representation; SMILES_canonical provides a canonical representation. structure_key groups molecular components, and structure_status records parsing outcomes.

Data quality

• experiment: Labels are measured in a lab.
• many sources: Compiled from several labs or papers, whose protocols differ.
• all structures readable: All 34,374 SMILES were read with RDKit.
• 16% undefined stereo: 16% of molecules have a stereocenter whose configuration is not given, so mirror-image forms cannot be told apart.
• 204 of 443 stereoisomer sets differ: Found 443 groups of molecules that differ only in stereochemistry. In 204, the values are 10-fold apart (log-scale labels), more than one standard deviation apart, or in different classes. That can be a real effect or a mistake in the source; a model that ignores stereochemistry treats these molecules as the same.
• 30 extreme values: 30 of 34,374 values lie more than 6 robust standard deviations from the median of their target (on a log scale when values span 1,000-fold or more). They are often typing or unit mistakes, and sometimes real; look at them before training.
• 4,815 labeled molecules: 4,815 molecules have a label.
• no source split: Make your own split. A random split puts near-identical molecules in both training and test sets, so results look better than they will on new compounds. Splitting by scaffold, or by date when dates are given, gives a more realistic estimate.

Dataset notes

• A component alone is not an independently measured molecule: labels apply to the whole mixture and its conditions.
• Composition, temperature, pressure, and units describe each mixture measurement.
• Components include ions and polymer repeat units.

License

CC BY4.0 (original dataset license documented in paper AppendixA1 and/or preserved Croissant); benchmark code MIT

Citation

Cite the original study and data source:

• CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction (NeurIPS 2025)
• ChemMix viscosity dataset provenance
• Original CheMixHub paper AppendixA1 licenses, Table1 experimental/simulation provenance