Predicted room temperature electrical conductivity of molecular mixtures

In the associated manuscript, we propose the MolSets machine learning model for molecular mixture properties. Using the MolSets architecture, we train a model on a dataset curated by Bradford et al. (2023) to predict the room temperature (298 K) electrical conductivity of mixtures. Here, we report the model-predicted conductivities of all equal-weight binary mixtures among 28 types of small molecules, combined with 30 types of Li+ salts (1 mol·kg-1), totaling 11,340 candidate lithium battery electrolytes. Note that the current model has a limitation of not taking salt solubility into account. This dataset is for demonstration purposes and should be used with caution., , , # Predicted room temperature electrical conductivity of molecular mixtures

[https://doi.org/10.5061/dryad.pnvx0k6w1](https://doi.org/10.5061/dryad.pnvx0k6w1)

This dataset reports the machine learning-predicted room temperature (298 K) electrical conductivity for 11,340 molecular mixtures.

## Description of the data and file structure

Every mixture consists of two types of molecules and one salt. The data is organized in a spreadsheet format. The columns are organized as follows:

* The first two columns, \"Solvent 1/2\" contain the SMILES strings of constituent molecules in the mixture.
* \"Weights\" and \"Molar Mass\" columns contain the weight fractions and molar masses (g/mol) of constituent molecules, respectively.
* The \"Salt\" column contains the SMILES string of the salts, whose amount (kg/mol) in the mixture is reported in the \"Molality\" column.
* The last column contains the predicted logarithm conductivities, in the unit of log (S/cm).

## Sharing/Access information

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