Add citation.cff and cite preprint in README and docs

CITATION.cff

@@ -0,0 +1,96 @@
+cff-version: 1.2.0
+title: presto
+message: >-
+  If you use presto in your work, please cite the article
+  below.
+type: software
+authors:
+  - name: The presto Contributors
+identifiers:
+  - type: doi
+    value: 10.26434/chemrxiv.15004169/v2
+    description: ChemRxiv Pre-Print
+repository-code: 'https://github.com/cole-group/presto'
+url: 'https://cole-group.github.io/presto/'
+abstract: >-
+  Molecular mechanics force fields enable the thorough
+  sampling required for applications such as alchemical
+  binding free energy calculations for computer-aided drug
+  design. However, parameters from transferable molecular
+  mechanics force fields can be unreliable, and training
+  accurate molecule-specific parameters against quantum
+  mechanical (QM) reference data is slow. Here, we present
+  presto (https://github.com/cole-group/presto), a Python
+  package for fast training of bespoke SMIRKS-Native Open
+  Force Field (SMIRNOFF) format force fields. presto
+  generates training data using high-temperature molecular
+  dynamics with well-tempered metadynamics. It iteratively
+  fits all valence parameters to energies and forces from
+  transferable machine learning potentials (MLPs). By
+  leveraging fast MLP evaluations and training on the GPU
+  using PyTorch (via the packages smee and descent), the
+  fully-automated workflow completes in around 15 minutes
+  for a 50-atom molecule on a single GPU. presto reduces
+  torsion scan errors by a factor of three relative to Open
+  Force Field Sage 2.3.0 on 400 molecules from TorsionNet500.
+  It also achieves torsion scan performance comparable to
+  direct QM fitting on a benchmark dataset of fragmented
+  drug-like molecules, and relative conformer energies of
+  510 molecules from the set of Folmsbee and Hutchison are
+  significantly improved compared to both Sage and espaloma.
+  presto allows simultaneous fitting of congeneric series
+  with partially shared parameters. It produces comparable
+  relative binding free energy performance to OpenFF 1.3.1
+  (Parsley) on systems where Parsley already performs well,
+  and may improve performance in cases where initial
+  parameters are particularly poor.
+keywords:
+  - force field
+  - free energy
+  - molecular mechanics
+  - machine learning potential
+  - openff
+  - smirnoff
+  - smee
+  - descent
+license: MIT
+
+preferred-citation:
+  type: article
+  title: >-
+    Fast Training of Bespoke SMIRNOFF-format Molecular
+    Mechanics Force Fields Using Machine Learning Potentials
+  authors:
+    - given-names: Finlay
+      family-names: Clark
+      orcid: 'https://orcid.org/0000-0003-0474-5475'
+    - given-names: Thomas
+      family-names: Pope
+      orcid: 'https://orcid.org/0000-0001-7552-9812'
+    - given-names: Sarah
+      family-names: Maier
+      orcid: 'https://orcid.org/0000-0002-3817-1476'
+    - given-names: Simon
+      family-names: Boothroyd
+      orcid: 'https://orcid.org/0000-0002-3456-1872'
+    - given-names: Joshua T.
+      family-names: Horton
+      orcid: 'https://orcid.org/0000-0001-8694-7200'
+    - given-names: Kevin
+      family-names: Ryczko
+      orcid: 'https://orcid.org/0000-0001-6933-3856'
+    - given-names: Andrea
+      family-names: Bortolato
+      orcid: 'https://orcid.org/0000-0002-1631-6363'
+    - given-names: Daniel J.
+      family-names: Cole
+      orcid: 'https://orcid.org/0000-0003-2933-0719'
+  identifiers:
+    - type: doi
+      value: 10.26434/chemrxiv.15004169/v2
+      description: ChemRxiv Pre-Print
+  doi: "10.26434/chemrxiv.15004169/v2"
+  journal: "ChemRxiv"
+  year: 2026
+  publisher:
+    name: ChemRxiv

README.md

@@ -70,6 +70,16 @@ presto train --param-settings.molecule-input-type sdf --param-settings.molecules
 For more details on the theory and implementation, please see the [documentation](https://cole-group.github.io/presto/latest/).
 
 
+## Citation
+
+If you use `presto` in your work, please cite:
+
+> Clark, F.; Pope, T.; Maier, S.; Boothroyd, S.; Horton, J. T.; Ryczko, K.; Bortolato, A.; Cole, D. J. *Fast Training of Bespoke SMIRNOFF-format Molecular Mechanics Force Fields Using Machine Learning Potentials.* ChemRxiv **2026**. [doi:10.26434/chemrxiv.15004169/v2](https://doi.org/10.26434/chemrxiv.15004169/v2)
+
+Because `presto` builds on the Open Force Field ecosystem, please also cite the relevant OpenFF publications listed at [openforcefield.org/science/how-to-cite](https://openforcefield.org/science/how-to-cite/).
+
+A `CITATION.cff` file is provided in the repository, and GitHub will generate formatted citations (including BibTeX) from the *Cite this repository* link on the project page.
+
 ## Copyright
 
 Copyright (c) 2025-2026, Finlay Clark, Newcastle University, UK

docs/changelog.md

@@ -4,6 +4,7 @@
 
 ### Documentation
 
+- Add a `CITATION.cff` file and cite the presto preprint in the README and docs, and point users to the OpenFF publications to cite, in [#76](https://github.com/cole-group/presto/pull/76).
 - Document installing `presto` from `conda-forge` as `presto-fit` (note this comes without the MLP dependencies, which must be installed separately) in [#70](https://github.com/cole-group/presto/pull/70).
 
 ## 0.8.1

docs/citing.md

@@ -0,0 +1,32 @@
+# Citing presto
+
+If you use `presto` in your work, please cite the following article:
+
+> Clark, F.; Pope, T.; Maier, S.; Boothroyd, S.; Horton, J. T.; Ryczko, K.; Bortolato, A.; Cole, D. J.
+> *Fast Training of Bespoke SMIRNOFF-format Molecular Mechanics Force Fields Using Machine Learning Potentials.*
+> ChemRxiv **2026**. [doi:10.26434/chemrxiv.15004169/v2](https://doi.org/10.26434/chemrxiv.15004169/v2)
+
+Because `presto` builds on the Open Force Field ecosystem, please also cite the
+relevant OpenFF publications listed at
+[openforcefield.org/science/how-to-cite](https://openforcefield.org/science/how-to-cite/).
+
+## BibTeX
+
+```bibtex
+@article{clark2026presto,
+  title   = {Fast Training of Bespoke {SMIRNOFF}-format Molecular Mechanics Force Fields Using Machine Learning Potentials},
+  author  = {Clark, Finlay and Pope, Thomas and Maier, Sarah and Boothroyd, Simon and Horton, Joshua T. and Ryczko, Kevin and Bortolato, Andrea and Cole, Daniel J.},
+  journal = {ChemRxiv},
+  year    = {2026},
+  doi     = {10.26434/chemrxiv.15004169/v2},
+  url     = {https://doi.org/10.26434/chemrxiv.15004169/v2},
+  publisher = {ChemRxiv}
+}
+```
+
+## CITATION.cff
+
+The repository also ships a [`CITATION.cff`](https://github.com/cole-group/presto/blob/main/CITATION.cff)
+file. On GitHub, use the **Cite this repository** button on the
+[project page](https://github.com/cole-group/presto) to generate a formatted
+citation (APA or BibTeX) automatically.

mkdocs.yml

@@ -56,6 +56,7 @@ nav:
   - Contributing: development/contributing.md
   - Building the docs: development/docs.md
   - Releasing: development/releasing.md
+- Citing presto: citing.md
 - Changelog: changelog.md
 
 theme: