CiderPress: Machine-Learned Exchange-Correlation Functionals
=============================================================

CiderPress implements the CIDER framework for constructing and evaluating
machine-learned density functionals.  The code serves several purposes:

* Specify the physically constrained electronic descriptors used as model
  inputs for CIDER functionals, along with the numerical settings for
  evaluating these features.
* Train, evaluate, store, and load Gaussian process regression models
  representing CIDER functionals. Tools are also included to map
  Gaussian processes to more efficient inference-time models.
* Through interfaces to existing DFT backends
  (PySCF :footcite:p:`sunRecentDevelopmentsPySCF2020a`
  and GPAW :footcite:p:`mortensenGPAWOpenPython2024`), compute
  the electronic input descriptors, evaluate the XC energy and potential,
  and perform full self-consistent field calculations with CIDER functionals.

CIDER stands for *Compressed scale-Invariant DEnsity Representation*.  The
name originally described the scale-invariant density features used
to learn exchange.  The framework now also includes smoothed
density-matrix features, full exchange-correlation models, and molecular and
periodic numerical implementations.

Getting started
---------------

* To run a calculation, begin with :doc:`installation/installation`, then use
  :doc:`usage/production_models` and :doc:`usage/quickstart`.
* To understand the functional forms used in CiderPress, begin with
  :doc:`theory/framework` and :doc:`features/features`.
* To inspect models, generate descriptors, or work on the implementation, use
  :doc:`workflows/workflows` and the API reference.

The calculation guides state which combinations of models, settings, and DFT
backends are supported.  In brief, PySCF is the molecular all-electron backend,
while classic GPAW provides the periodic plane-wave PAW implementation.
The packaged functional families compute different parts of the XC energy.
CIDER23X and CIDER24X use an explicit surrogate-hybrid composition;
CIDER26XC uses the full-XC initialization described in the model guide.

.. toctree::
   :maxdepth: 2
   :caption: Getting started

   installation/installation
   usage/production_models
   usage/quickstart

.. toctree::
   :maxdepth: 2
   :caption: Running calculations

   usage/pyscf
   usage/gpaw
   usage/properties
   usage/convergence

.. toctree::
   :maxdepth: 2
   :caption: The CIDER framework

   theory/theory
   features/features

.. toctree::
   :maxdepth: 2
   :caption: Model and descriptor workflows

   workflows/workflows

.. toctree::
   :maxdepth: 2
   :caption: API and implementation reference

   ciderpress/dft/dft
   ciderpress/models/models
   ciderpress/pyscf/pyscf
   ciderpress/gpaw/gpaw
   c_extensions/c_extensions

.. toctree::
   :maxdepth: 2
   :caption: Reference

   reference/limitations
   reference/citing

.. footbibliography::