Possible fix for publication.

content/publication/journal-article/cite.bib

@@ -1,8 +1,14 @@
-@article{example2,
-  title = {An example journal article},
-  author={Bighetti, Nelson and Ford, Robert},
-  journal = {Journal of Source Themes},
-  year    = 2015,
-  volume  = 1,
-  number  = 1
-}
+@article{Chen2023,
+   abstract = {<p>A strategy is presented for the machine-learning emulation of electronic structure calculations carried out in the electronically grand-canonical ensemble. The approach relies upon a dual-learning scheme, where both the system charge and the system energy are predicted for each image. The scheme is shown to be capable of emulating basic electrochemical reactions at a range of potentials, and coupling it with a bootstrap-ensemble approach gives reasonable estimates of the prediction uncertainty. The method is also demonstrated to accelerate saddle-point searches, and to extrapolate to systems with one to five water layers. We anticipate that this method will allow for larger length- and time-scale simulations necessary for electrochemical simulations.</p>},
+   author = {Xi Chen and Muammar El Khatib and Per Lindgren and Adam Willard and Andrew J. Medford and Andrew A. Peterson},
+   doi = {10.1038/s41524-023-01007-6},
+   issn = {2057-3960},
+   issue = {1},
+   journal = {npj Computational Materials},
+   month = {5},
+   pages = {73},
+   title = {Atomistic learning in the electronically grand-canonical ensemble},
+   volume = {9},
+   url = {https://www.nature.com/articles/s41524-023-01007-6},
+   year = {2023},
+}

content/publication/journal-article/index.md

@@ -1,30 +1,34 @@
 ---
-title: "An example journal article"
+title: "Atomistic learning in the electronically grand-canonical ensemble"
 authors:
+- Xi Chen
 - admin
-- Robert Ford
+- Per Lindgren
+- Adam Willard
+- Andrew J. Medford
+- Andrew A. Peterson
 author_notes:
-- "Equal contribution"
-- "Equal contribution"
-date: "2015-09-01T00:00:00Z"
+# - "Equal contribution"
+# - "Equal contribution"
+date: "2023-05-11T00:00:00Z"
 doi: ""
 
 # Schedule page publish date (NOT publication's date).
-publishDate: "2017-01-01T00:00:00Z"
+publishDate: "2023-05-11T00:00:00Z"
 
 # Publication type.
 # Accepts a single type but formatted as a YAML list (for Hugo requirements).
 # Enter a publication type from the CSL standard.
 publication_types: ["article-journal"]
 
 # Publication name and optional abbreviated publication name.
-publication: "*Journal of Source Themes, 1*(1)"
+publication: npj Computational Materials
 publication_short: ""
 
-abstract: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.
+abstract: A strategy is presented for the machine-learning emulation of electronic structure calculations carried out in the electronically grand-canonical ensemble. The approach relies upon a dual-learning scheme, where both the system charge and the system energy are predicted for each image. The scheme is shown to be capable of emulating basic electrochemical reactions at a range of potentials, and coupling it with a bootstrap-ensemble approach gives reasonable estimates of the prediction uncertainty. The method is also demonstrated to accelerate saddle-point searches, and to extrapolate to systems with one to five water layers. We anticipate that this method will allow for larger length- and time-scale simulations necessary for electrochemical simulations.
 
 # Summary. An optional shortened abstract.
-summary: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.
+summary: Atomistic learning in the electronically grand-canonical ensemble..
 
 tags:
 - Source Themes
@@ -33,8 +37,8 @@ featured: false
 # links:
 # - name: ""
 #   url: ""
-url_pdf: http://arxiv.org/pdf/1512.04133v1
-url_code: 'https://github.com/HugoBlox/hugo-blox-builder'
+url_pdf: https://www.nature.com/articles/s41524-023-01007-6
+url_code: 'https://bitbucket.org/andrewpeterson/amp'
 url_dataset: ''
 url_poster: ''
 url_project: ''
@@ -45,7 +49,7 @@ url_video: ''
 # Featured image
 # To use, add an image named `featured.jpg/png` to your page's folder. 
 image:
-  caption: 'Image credit: [**Unsplash**](https://unsplash.com/photos/jdD8gXaTZsc)'
+  caption: 'Image credit: Xi Chen.'
   focal_point: ""
   preview_only: false