Add 4 static files and update faq links (#159)

ui/public/An_electrochemical_series_for_materials.bib

@@ -0,0 +1,22 @@
+
+
+
+
+
+
+
+
+@article{
+doi:10.1073/pnas.2320134121,
+author = {Tim Mueller  and Joseph Montoya  and Weike Ye  and Xiangyun Lei  and Linda Hung  and Jens Hummelshøj  and Michael Puzon  and Daniel Martinez  and Chris Fajardo  and Rachel Abela },
+title = {An electrochemical series for materials},
+journal = {Proceedings of the National Academy of Sciences},
+volume = {121},
+number = {38},
+pages = {e2320134121},
+year = {2024},
+doi = {10.1073/pnas.2320134121},
+URL = {https://www.pnas.org/doi/abs/10.1073/pnas.2320134121},
+eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.2320134121},
+abstract = {Chemists and materials scientists often use the concept of oxidation states, the number of electrons a species has effectively gained or lost, to understand and predict chemical reactions and material properties. The list of chemical species sorted by how easily they gain electrons is known as the electrochemical series. The standard electrochemical series is largely based on data in aqueous solutions, which does not always translate well to solid-state materials. We have created an electrochemical series that is appropriate for use in inorganic materials by developing a physical model of oxidation states in materials and parameterizing the model using machine learning. We demonstrate applications of this approach to oxidation state prediction, structure prediction, materials chemistry, and materials discovery. The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. To address this problem, we have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this series is generally more consistent with oxidation states in solid-state materials than the series based on aqueous ions. The electrochemical series was constructed by developing and parameterizing a physical, human-interpretable model of oxidation states in materials. We show that this model enables the prediction of oxidation states from composition in a way that is more accurate than a state-of-the-art transformer-based neural network model. We present applications of our approach to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available website and API.}}
+

ui/public/An_electrochemical_series_for_materials.enw

@@ -0,0 +1,31 @@
+
+
+
+
+
+
+
+%0 Journal Article
+%A Mueller, Tim
+%A Montoya, Joseph
+%A Ye, Weike
+%A Lei, Xiangyun
+%A Hung, Linda
+%A Hummelshøj, Jens
+%A Puzon, Michael
+%A Martinez, Daniel
+%A Fajardo, Chris
+%A Abela, Rachel
+%T An electrochemical series for materials
+%D 2024
+%J Proceedings of the National Academy of Sciences
+%P e2320134121
+%V 121
+%N 38
+%R doi:10.1073/pnas.2320134121
+%U https://www.pnas.org/doi/abs/10.1073/pnas.2320134121
+%X Chemists and materials scientists often use the concept of oxidation states, the number of electrons a species has effectively gained or lost, to understand and predict chemical reactions and material properties. The list of chemical species sorted by how easily they gain electrons is known as the electrochemical series. The standard electrochemical series is largely based on data in aqueous solutions, which does not always translate well to solid-state materials. We have created an electrochemical series that is appropriate for use in inorganic materials by developing a physical model of oxidation states in materials and parameterizing the model using machine learning. We demonstrate applications of this approach to oxidation state prediction, structure prediction, materials chemistry, and materials discovery. The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. To address this problem, we have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this series is generally more consistent with oxidation states in solid-state materials than the series based on aqueous ions. The electrochemical series was constructed by developing and parameterizing a physical, human-interpretable model of oxidation states in materials. We show that this model enables the prediction of oxidation states from composition in a way that is more accurate than a state-of-the-art transformer-based neural network model. We present applications of our approach to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available website and API.
+
+
+
+

ui/public/An_electrochemical_series_for_materials.ris

@@ -0,0 +1,31 @@
+
+TY  - JOUR
+T1  - An electrochemical series for materials
+AU  - Mueller, Tim
+AU  - Montoya, Joseph
+AU  - Ye, Weike
+AU  - Lei, Xiangyun
+AU  - Hung, Linda
+AU  - Hummelshøj, Jens
+AU  - Puzon, Michael
+AU  - Martinez, Daniel
+AU  - Fajardo, Chris
+AU  - Abela, Rachel
+Y1  - 2024/09/17
+PY  - 2024
+DA  - 2024/09/17
+N1  - doi: 10.1073/pnas.2320134121
+DO  - 10.1073/pnas.2320134121
+T2  - Proceedings of the National Academy of Sciences
+JF  - Proceedings of the National Academy of Sciences
+JO  - Proceedings of the National Academy of Sciences
+SP  - e2320134121
+VL  - 121
+IS  - 38
+PB  - Proceedings of the National Academy of Sciences
+M3  - doi: 10.1073/pnas.2320134121
+UR  - https://doi.org/10.1073/pnas.2320134121
+Y2  - 2024/09/09
+N2  - Chemists and materials scientists often use the concept of oxidation states, the number of electrons a species has effectively gained or lost, to understand and predict chemical reactions and material properties. The list of chemical species sorted by how easily they gain electrons is known as the electrochemical series. The standard electrochemical series is largely based on data in aqueous solutions, which does not always translate well to solid-state materials. We have created an electrochemical series that is appropriate for use in inorganic materials by developing a physical model of oxidation states in materials and parameterizing the model using machine learning. We demonstrate applications of this approach to oxidation state prediction, structure prediction, materials chemistry, and materials discovery. The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. To address this problem, we have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this series is generally more consistent with oxidation states in solid-state materials than the series based on aqueous ions. The electrochemical series was constructed by developing and parameterizing a physical, human-interpretable model of oxidation states in materials. We show that this model enables the prediction of oxidation states from composition in a way that is more accurate than a state-of-the-art transformer-based neural network model. We present applications of our approach to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available website and API.
+AB  - Chemists and materials scientists often use the concept of oxidation states, the number of electrons a species has effectively gained or lost, to understand and predict chemical reactions and material properties. The list of chemical species sorted by how easily they gain electrons is known as the electrochemical series. The standard electrochemical series is largely based on data in aqueous solutions, which does not always translate well to solid-state materials. We have created an electrochemical series that is appropriate for use in inorganic materials by developing a physical model of oxidation states in materials and parameterizing the model using machine learning. We demonstrate applications of this approach to oxidation state prediction, structure prediction, materials chemistry, and materials discovery. The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. To address this problem, we have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this series is generally more consistent with oxidation states in solid-state materials than the series based on aqueous ions. The electrochemical series was constructed by developing and parameterizing a physical, human-interpretable model of oxidation states in materials. We show that this model enables the prediction of oxidation states from composition in a way that is more accurate than a state-of-the-art transformer-based neural network model. We present applications of our approach to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available website and API.
+ER  - 

ui/src/constants/links.ts

@@ -1,3 +1,5 @@
 export const MANUSCRIPT_LINK = 'https://dx.doi.org/10.1073/pnas.2320134121';
 
 export const GITHUB_LINK = 'https://github.com/TRI-AMDD/oxidation-state-api-public';
+
+export const PREPRINT_LINK = 'https://doi.org/10.26434/chemrxiv-2023-9dvlm-v3';

ui/src/pages/FAQ/faq-util/faq-text.tsx

@@ -1,4 +1,4 @@
-import { MANUSCRIPT_LINK, GITHUB_LINK } from '@/constants/links';
+import { MANUSCRIPT_LINK, GITHUB_LINK, PREPRINT_LINK } from '@/constants/links';
 
 export interface FAQText {
     question: string;
@@ -10,8 +10,15 @@ export const URL_FAQ_STRING_MATCH = 'faq-';
 export const FAQ_ITEMS: FAQText[] = [
     {
         question: 'How can I cite the Oxidation State Analyzer?',
-        //answer: 'If you would like to cite the oxidation state analyzer, please reference the following manuscript: PLACEHOLDER. You can directly export this reference to a citation manager by clicking on one of the following links: PLACEHOLDER'
-        answer: `A preprint of our manuscript is available <a href="${MANUSCRIPT_LINK}" rel="noopener noreferrer" target="_blank">here </a>.`
+        answer: `
+            Please cite the following paper:
+            <br />T. Mueller, J. Montoya, W. Ye, X. Lei, L. Hung, J. Hummelshøj, M. Puzon, D. Martinez, C. Fajardo, and R. Abela, An electrochemical series for materials. Proceedings of the National Academy of Sciences, 2024. 121(38): p. e2320134121
+            <br />The publication DOI is 10.1073/pnas.2320134121. You can download this reference in 
+            <a rel='noopener noreferrer' target='_blank' href='https://www.oxi.matr.io/An_electrochemical_series_for_materials.ris'>RIS</a>, 
+            <a rel='noopener noreferrer' target='_blank' href='https://www.oxi.matr.io/An_electrochemical_series_for_materials.enw'>EndNote</a>, or 
+            <a rel='noopener noreferrer' target='_blank' href='https://www.oxi.matr.io/An_electrochemical_series_for_materials.bib'>BibTeX</a> 
+            formats.  A <a href="${PREPRINT_LINK}" rel="noopener noreferrer" target="_blank">preprint</a> of this paper is available on ChemRxiv.
+        `
     },
     {
         question: 'For what species can oxidation states be calculated?',
@@ -67,6 +74,11 @@ export const FAQ_ITEMS: FAQText[] = [
     },
     {
         question: 'Can I download all of the oxidation state ranges?',
-        answer: `The oxidation state ranges for all available species can be downloaded in JSON format <a rel='noopener noreferrer' target='_blank' href='https://www.oxi.matr.io/oxidation_boundaries.json'>here</a>. The units are the same as those used on this web site.`
+        answer: `
+        The oxidation state ranges for all available species can be downloaded in JSON format <a rel='noopener noreferrer' target='_blank' href='https://www.oxi.matr.io/oxidation_boundaries.json'>here</a>. The units are the same as those used on this web site.
+        <br /> You can also download a high-resolution 
+        <a rel='noopener noreferrer' target='_blank' href='https://www.oxi.matr.io/IDES_all_elements.pdf'>graphical representation</a> 
+        of the oxidation state ranges for all 84 elements included in our model.
+        `
     }
 ];