> ## 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. # Direct Entries > Drop coherent literature parameter sets into a pipeline with ionworks-schema direct entries. A direct entry populates parameter values without doing any calculation or fitting. There are two shapes: * **`iws.direct_entries.DirectEntry`** — a flat dict of values you supply yourself. * **`iws.direct_entries.*`** function-schema subclasses (e.g. `LandesfeindElectrolyte`) — pre-built parameterisations from the literature. For the physics behind the electrolyte parameterisations and when to pick each one, see the [Electrolyte direct entries Guide](/guide/pipelines/direct-entries/electrolyte) and [Electrolyte transport](/guide/modeling/electrolyte-transport). ## Custom DirectEntry ```python theme={null} import ionworks_schema as iws known = iws.direct_entries.DirectEntry( parameters={ "Ambient temperature [K]": 298.15, "Nominal cell capacity [A.h]": 3.0, "Lower voltage cut-off [V]": 2.5, "Upper voltage cut-off [V]": 4.2, }, source="manufacturer datasheet", ) pipeline = iws.Pipeline({"known": known}) ``` `parameters` accepts floats, arrays, and pybamm-serialisable symbols. ### Callable parameters via `pybamm.ParameterValues` For parameters that are callables — for example, concentration- or temperature-dependent interpolants — wrap them in a `pybamm.ParameterValues` and pass that. Its serialisation converts each callable into the symbolic form the server reconstructs on the other side. A raw `dict` containing callables is **not** auto-serialised; you must wrap it explicitly. ```python theme={null} import numpy as np import pybamm import ionworks_schema as iws x = np.linspace(100.0, 3000.0, 6) pv = pybamm.ParameterValues( { "Electrolyte conductivity [S.m-1]": lambda c_e, T: pybamm.Interpolant( x, 0.1 + 1e-3 * x, c_e, name="conductivity" ), "Electrode height [m]": 0.1, } ) known = iws.direct_entries.DirectEntry(parameters=pv, source="custom fit") ``` The interpolant lambda's signature must be exactly `(c_e, T)` (or the relevant pybamm input variables) — capture any extra inputs (lookup arrays, names) via closure rather than default kwargs, since defaults are inferred as function inputs during serialisation and break reconstruction. ## Electrolyte direct entries | Schema | What it sets | | ---------------------------------------------------------------- | ------------------------------------------------------------------------------ | | `iws.direct_entries.ConstantElectrolyte(c_e=...)` | Initial salt concentration only | | `iws.direct_entries.NymanElectrolyte(c_e=...)` | $\kappa(c_e)$, $D_e(c_e)$, $\chi$, $t_+^0$ from Nyman et al. 2008 (isothermal) | | `iws.direct_entries.LandesfeindElectrolyte(c_e=..., system=...)` | Full $T$- and $c_e$-dependent set from Landesfeind & Gasteiger 2019 | | `iws.direct_entries.ArrheniusElectrolyteDiffusivity()` | Wraps a reference $D_e(c_e)$ in an Arrhenius temperature factor | | `iws.direct_entries.ArrheniusElectrolyteConductivity()` | Wraps a reference $\kappa(c_e)$ in an Arrhenius temperature factor | ```python theme={null} import ionworks_schema as iws from ionworks import Ionworks electrolyte = iws.direct_entries.LandesfeindElectrolyte( c_e=1000.0, system="EC:EMC (3:7)", ) pipeline = iws.Pipeline({"electrolyte": electrolyte}) client = Ionworks() submission = client.pipeline.create(pipeline) ``` `system` must be one of `"EC:DMC (1:1)"`, `"EC:EMC (3:7)"`, or `"EMC:FEC (19:1)"`. ## Building electrolyte transport from a material dataset If you have measured electrolyte transport properties (conductivity, diffusivity, transference number, thermodynamic factor) versus concentration stored as a [material property dataset](/data/materials), `client.electrolyte.transport_from_dataset()` turns the dataset into a `pybamm.ParameterValues` of concentration-dependent functions that you can drop straight into a `DirectEntry`. Each property is represented in one of two ways, chosen per-parameter: * `"interpolant"` (default) — a tabulated `pybamm.Interpolant` of the measured points with linear extrapolation. * `"landesfeind"` — the isothermal [Landesfeind & Gasteiger (2019)](https://doi.org/10.1149/2.0571912jes) functional form for that property, fitted to the measured points. Unlike a tabulated interpolant, the fitted conductivity and diffusivity forms stay positive and finite below the lowest measured concentration, which keeps high-rate DFN solves stable when the electrolyte depletes near an electrode. ```python theme={null} from ionworks import Ionworks import ionworks_schema as iws client = Ionworks() params = client.electrolyte.transport_from_dataset( "dataset-uuid", forms={ "Electrolyte conductivity [S.m-1]": "landesfeind", "Electrolyte diffusivity [m2.s-1]": "landesfeind", "Cation transference number": "interpolant", }, ) electrolyte = iws.direct_entries.DirectEntry(parameters=params, source="in-house measurements") pipeline = iws.Pipeline({"electrolyte": electrolyte}) submission = client.pipeline.create(pipeline) ``` ### Arguments | Argument | Description | | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `dataset_id` | UUID of the [material property dataset](/data/materials) holding the transport properties versus electrolyte concentration. | | `forms` | Optional mapping from pybamm parameter name to `"interpolant"` or `"landesfeind"`. Parameters not listed default to `"interpolant"`. | | `columns` | Optional mapping from pybamm parameter name to dataset column name. Defaults to `{"Electrolyte conductivity [S.m-1]": "Electrolyte conductivity", "Electrolyte diffusivity [m2.s-1]": "Electrolyte diffusivity", "Cation transference number": "Transference number"}`. Add a thermodynamic-factor entry here if your dataset includes it, e.g. `{"Thermodynamic factor": "My TDF column"}` (the key is the pybamm parameter name `Thermodynamic factor`, the value is your dataset's column name). | | `concentration_column` | Name of the electrolyte-concentration column (in mol.m-3) in the dataset. Defaults to `Electrolyte concentration`. | The `"landesfeind"` form is available for `Electrolyte conductivity [S.m-1]`, `Electrolyte diffusivity [m2.s-1]`, `Cation transference number`, and `Thermodynamic factor`. The dataset is treated as isothermal — each fitted form depends only on concentration. ## Piecewise interpolant direct entries For SOC- or temperature-dependent parameters, use `iws.direct_entries.PiecewiseInterpolation1D` or `PiecewiseInterpolation2D`. See [Calculations → Piecewise](/pipelines/calculations/piecewise). ## Fitting coefficients from a direct entry The transport-property coefficients inside `LandesfeindElectrolyte` are exposed as named parameters precisely so they can be overridden as fit unknowns. Put the entry into a pipeline and reference the same parameter names in a downstream [`DataFit`](/pipelines/data-fitting/overview) — the published values act as the base and the optimizer searches over the overridden ones. Physics behind the four transport properties. How direct entries fit into the wider pipeline.