> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ionworks.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Electrolyte direct entries

> Direct entries that set the four electrolyte transport properties for a DFN model from literature values or as fit unknowns.

A direct entry populates parameter values without doing any calculation or fitting — useful for dropping a coherent literature parameter set into a pipeline. The four electrolyte direct entries cover the [four electrolyte transport properties](/guide/modeling/electrolyte-transport) needed by a binary-electrolyte DFN model.

## Available entries

<CardGroup cols={2}>
  <Card title="ConstantElectrolyte" icon="circle-dot">
    Just sets the initial salt concentration; everything else is left to defaults or other entries.
  </Card>

  <Card title="NymanElectrolyte" icon="book">
    Full set for LiPF$_6$ in EC:EMC from [Nyman et al. 2008](https://doi.org/10.1016/j.electacta.2008.04.023). Concentration-dependent $\kappa$ and $D_e$, constant $\chi=1$ and $t_+^0 = 0.2594$. Isothermal.
  </Card>

  <Card title="LandesfeindElectrolyte" icon="temperature-half">
    Full set with concentration **and** temperature dependence for three solvent systems (`EC:DMC (1:1)`, `EC:EMC (3:7)`, `EMC:FEC (19:1)`) from [Landesfeind & Gasteiger 2019](https://doi.org/10.1149/2.0571912jes).
  </Card>

  <Card title="ArrheniusElectrolyte*" icon="chart-line">
    `ArrheniusElectrolyteDiffusivity` and `ArrheniusElectrolyteConductivity` wrap a reference $D_e(c_e)$ or $\kappa(c_e)$ in an Arrhenius temperature factor. Useful when you have isothermal data and need to bolt on $T$-dependence.
  </Card>
</CardGroup>

A direct entry returns the initial salt concentration plus all four transport-property functions and the coefficients those functions reference. Plugging it into a pipeline is enough to fully specify the electrolyte block of a DFN model.

## Fitting your own coefficients

The six conductivity coefficients (and the diffusivity, thermodynamic-factor, and transference-number coefficients) inside `LandesfeindElectrolyte` are exposed as named parameters precisely so they can be replaced with fit unknowns. Override the relevant parameter names in the data-fit `parameters` dict and run a pipeline that includes the direct entry — the published values act as the base and the optimizer searches over the overridden ones.

<Note>
  To configure and submit the Landesfeind/Nyman direct entries with `ionworks-schema` + `ionworks-api`, see [Pipelines → Direct Entries](/pipelines/direct-entries).
</Note>

## Building transport from your own data

If you already have measured transport properties versus concentration for your electrolyte, you can skip the literature parameterisations and build a custom electrolyte direct entry from a [material property dataset](/data/materials) with `client.electrolyte.transport_from_dataset()`. Each property can be returned as a tabulated interpolant or as a fitted isothermal Landesfeind form — see [Pipelines → Direct Entries](/pipelines/direct-entries#building-electrolyte-transport-from-a-material-dataset).

<Note>
  For the physics behind the four transport properties and how they are measured experimentally, see [Electrolyte transport properties](/guide/modeling/electrolyte-transport).
</Note>