Adding ml4chem.

content/_index.md

@@ -106,8 +106,7 @@ sections:
                 representative before the “Machine Learning for Pharmaceutical Discovery
                 and Synthesis Consortium” collaboration with the Massachusetts Institute
                 of Technology. For more information, see https://mlpds.mit.edu/.
-                * Implement and apply uncertainty quantification of epistemic uncertainty
-                of predictive models to steer scientific discovery.
+                * Implement and apply uncertainty quantification of predictive models to steer scientific discovery.
                 * Supervise and mentor interns to develop their careers and benefit
                 therapeutic programs at BMS.
         - title: Postdoctoral Scholar

content/publication/preprint/cite.bib

@@ -0,0 +1,11 @@
+@article{Khatib2020,
+   abstract = {ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users. ML4Chem follows user-experience design and offers the needed tools to go from data preparation to inference. Here we introduce its atomistic module for the implementation, deployment, and reproducibility of atom-centered models. This module is composed of six core building blocks: data, featurization, models, model optimization, inference, and visualization. We present their functionality and easiness of use with demonstrations utilizing neural networks and kernel ridge regression algorithms.},
+   author = {Muammar El Khatib and Wibe A de Jong and Muammar El Khatib and Wibe A de Jong},
+   doi = {10.26434/chemrxiv.11952516.v1},
+   journal = {arXiv},
+   keywords = {chemistry,machine learning,materials,physics},
+   month = {3},
+   title = {ML4Chem : A Machine Learning Package for Chemistry and Materials Science},
+   url = {http://arxiv.org/abs/2003.13388},
+   year = {2020},
+}

content/publication/preprint/index.md

@@ -1,36 +1,37 @@
 ---
-title: "An example preprint / working paper"
+title: "ML4Chem : A Machine Learning Package for Chemistry and Materials Science"
 authors:
 - admin
-date: "2019-04-07T00:00:00Z"
+- Wibe de Jong
+date: "2020-03-01T00:00:00Z"
 doi: ""
 
 # Schedule page publish date (NOT publication's date).
-publishDate: "2017-01-01T00:00:00Z"
+publishDate: "2020-03-01T00:00:00Z"
 
 # 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"]
 
 # Publication name and optional abbreviated publication name.
-publication: ""
+publication: "ArXiv"
 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: 'ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users. ML4Chem follows user-experience design and offers the needed tools to go from data preparation to inference. Here we introduce its atomistic module for the implementation, deployment, and reproducibility of atom-centered models. This module is composed of six core building blocks: data, featurization, models, model optimization, inference, and visualization. We present their functionality and easiness of use with demonstrations utilizing neural networks and kernel ridge regression algorithms.'
 
 # 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: A machine learning package for chemistry and materials science.
 
 tags:
 - Source Themes
 featured: false
 
 links:
-- name: Custom Link
-  url: http://example.org
-url_pdf: http://arxiv.org/pdf/1512.04133v1
-url_code: 'https://github.com/HugoBlox/hugo-blox-builder'
+- name: Website
+  url: https://ml4chem.dev
+url_pdf: https://arxiv.org/pdf/2003.13388.pdf
+url_code: 'https://github.com/muammar/ml4chem'
 url_dataset: '#'
 url_poster: '#'
 url_project: ''
@@ -41,7 +42,7 @@ 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/s9CC2SKySJM)'
+  caption: 'Image credit: Muammar El Khatib'
   focal_point: ""
   preview_only: false
 
@@ -50,19 +51,19 @@ image:
 #   Simply enter your project's folder or file name without extension.
 #   E.g. `internal-project` references `content/project/internal-project/index.md`.
 #   Otherwise, set `projects: []`.
-projects:
-- internal-project
+# projects:
+# - internal-project
 
 # Slides (optional).
 #   Associate this publication with Markdown slides.
 #   Simply enter your slide deck's filename without extension.
 #   E.g. `slides: "example"` references `content/slides/example/index.md`.
 #   Otherwise, set `slides: ""`.
-slides: example
+# slides: example
 ---
 
-{{% callout note %}}
+<!-- {{% callout note %}}
 Create your slides in Markdown - click the *Slides* button to check out the example.
 {{% /callout %}}
 
-Add the publication's **full text** or **supplementary notes** here. You can use rich formatting such as including [code, math, and images](https://docs.hugoblox.com/content/writing-markdown-latex/).
+Add the publication's **full text** or **supplementary notes** here. You can use rich formatting such as including [code, math, and images](https://docs.hugoblox.com/content/writing-markdown-latex/). -->