From c6c7fa8680710e39491d49e46cef77f6b60d8f22 Mon Sep 17 00:00:00 2001
From: tinatn29 <tanaporn.nanarong@gmail.com>
Date: Tue, 1 Oct 2024 11:42:43 -0400
Subject: [PATCH 1/4] add tina's paper

---
 db/citations.yml | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)

diff --git a/db/citations.yml b/db/citations.yml
index 3484904e..777fbf95 100644
--- a/db/citations.yml
+++ b/db/citations.yml
@@ -8556,6 +8556,46 @@ nakam;jmchema17:
   wwwemail: ''
   wwwpub: http://slapper.apam.columbia.edu/bib-eu9iifae/papers/nakam_jmca17.pdf
   year: '2017'
+nanarong;npj24:
+  ackno: This work was funded by Toyota Research Institute, grant number PO-002332.
+  author:
+    - Tanaporn Na Narong
+    - Zoe N. Zachko
+    - Steven B. Torrisi
+    - Simon J. L. Billinge
+  doi:
+  entrytype: article
+  facility:
+  grant:
+  journal: Npj Comput. Mater.
+  month: oct
+  nb: ''
+  note: to be submitted
+  notes: ''
+  number: ''
+  optannote: ''
+  optnote: ''
+  optwwwlanl: ''
+  pages: ''
+  summary_professional: Interpretable machine learning (ML) provides a 
+    generalizable approach for combining information from multiple heterogeneous 
+    spectra. Random forest models were trained on X-ray absorption near-edge 
+    spectra (XANES), atomic pair distribution functions (PDFs), and both inputs 
+    combined, to extract local atomic environments of transition metal cations 
+    in oxides. Feature importance analysis revealed the most informative regions 
+    in both spectra and how the information is balanced between them. Our approach 
+    enables exploration of information content and can inform experimental design 
+    when choosing between different methods.
+  synopsis: Random forest models trained on XANES, PDF, and both combined 
+    and the feature importance analysis allow us to combine information 
+    from heterogeneous experimental spectra and investigate the information 
+    content and complementarity between the two inputs.
+  tags: ml, xanes, pdf, random-forest
+  title: 'Use of machine learning in experiment design for multi-modal analysis of materials: 
+  x-ray absorption near-edge spectra (XANES) and pair distribution functions (PDF)'
+  url: https://doi.org/
+  volume: ''
+  year: '2024'
 nguye;pnas19:
   author:
     - Andy I. Nguyen

From 1446b5650fd539d95c67fa49c1c5acbe8e2bdf46 Mon Sep 17 00:00:00 2001
From: tinatn29 <tanaporn.nanarong@gmail.com>
Date: Fri, 25 Oct 2024 15:04:08 -0400
Subject: [PATCH 2/4] add arxiv and corrected previous errors

---
 db/citations.yml | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/db/citations.yml b/db/citations.yml
index 777fbf95..fef9cc47 100644
--- a/db/citations.yml
+++ b/db/citations.yml
@@ -8563,14 +8563,14 @@ nanarong;npj24:
     - Zoe N. Zachko
     - Steven B. Torrisi
     - Simon J. L. Billinge
-  doi:
+  doi: 10.48550/arXiv.2410.17467
   entrytype: article
-  facility:
-  grant:
-  journal: Npj Comput. Mater.
-  month: oct
+  facility: ''
+  grant: TRI
+  journal: ''
+  month: ''
   nb: ''
-  note: to be submitted
+  note: submitted to npj Comput. Mater.
   notes: ''
   number: ''
   optannote: ''
@@ -8593,7 +8593,7 @@ nanarong;npj24:
   tags: ml, xanes, pdf, random-forest
   title: 'Use of machine learning in experiment design for multi-modal analysis of materials: 
   x-ray absorption near-edge spectra (XANES) and pair distribution functions (PDF)'
-  url: https://doi.org/
+  url: https://doi.org/10.48550/arXiv.2410.17467
   volume: ''
   year: '2024'
 nguye;pnas19:

From 06ef2f4a27761d1cc9791d93bc43f2d4f49c73c5 Mon Sep 17 00:00:00 2001
From: tinatn29 <tanaporn.nanarong@gmail.com>
Date: Mon, 28 Oct 2024 13:20:39 -0400
Subject: [PATCH 3/4] edit tina's paper details per SB comments

---
 db/citations.yml | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/db/citations.yml b/db/citations.yml
index fef9cc47..63e4331a 100644
--- a/db/citations.yml
+++ b/db/citations.yml
@@ -8566,11 +8566,11 @@ nanarong;npj24:
   doi: 10.48550/arXiv.2410.17467
   entrytype: article
   facility: ''
-  grant: TRI
-  journal: ''
+  grant: tri22
+  journal: arXiv
   month: ''
   nb: ''
-  note: submitted to npj Comput. Mater.
+  note: https://doi.org/10.48550/arXiv.2410.17467
   notes: ''
   number: ''
   optannote: ''
@@ -8586,10 +8586,8 @@ nanarong;npj24:
     in both spectra and how the information is balanced between them. Our approach 
     enables exploration of information content and can inform experimental design 
     when choosing between different methods.
-  synopsis: Random forest models trained on XANES, PDF, and both combined 
-    and the feature importance analysis allow us to combine information 
-    from heterogeneous experimental spectra and investigate the information 
-    content and complementarity between the two inputs.
+  synopsis: original paper demonstrating use of interpretable ML for combining
+    heterogeneous input spectra, XANES and PDF
   tags: ml, xanes, pdf, random-forest
   title: 'Use of machine learning in experiment design for multi-modal analysis of materials: 
   x-ray absorption near-edge spectra (XANES) and pair distribution functions (PDF)'

From 80fcacea78da125dad4725d97e4d8db953df137e Mon Sep 17 00:00:00 2001
From: tinatn29 <tanaporn.nanarong@gmail.com>
Date: Tue, 29 Oct 2024 10:24:59 -0400
Subject: [PATCH 4/4] add arxiv no. to note

---
 db/citations.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/db/citations.yml b/db/citations.yml
index 63e4331a..4f336276 100644
--- a/db/citations.yml
+++ b/db/citations.yml
@@ -8570,7 +8570,7 @@ nanarong;npj24:
   journal: arXiv
   month: ''
   nb: ''
-  note: https://doi.org/10.48550/arXiv.2410.17467
+  note: arXiv:2410.17467
   notes: ''
   number: ''
   optannote: ''