+layout.svelte add <svelte:head> with <title> and <meta description>

fix resetting h3 headings after each h2 more preprint work
janosh · Jun 20, 2023 · 5d38918 · 5d38918
1 parent e41ca32
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Showing 9 changed files with 127 additions and 12 deletions.
diff --git a/matbench_discovery/__init__.py b/matbench_discovery/__init__.py
@@ -4,7 +4,7 @@
 import sys
 from datetime import datetime
 
-ROOT = os.path.dirname(os.path.dirname(__file__))  # repo root
+ROOT = os.path.dirname(os.path.dirname(__file__))  # repo root directory
 FIGS = f"{ROOT}/site/src/figs"  # directory to store interactive figures
 STATIC = f"{ROOT}/site/static/figs"  # directory to store static figures, is symlinked
 # into site/src/routes/paper/figs dir

diff --git a/matbench_discovery/preds.py b/matbench_discovery/preds.py
@@ -33,12 +33,13 @@
 # pick F1 as primary metric to sort by
 df_metrics = df_metrics.round(3).sort_values("F1", axis=1)
 
-
+# dataframe of all models' energy above convex hull (EACH) predictions (eV/atom)
 df_each_pred = pd.DataFrame()
 for model in df_metrics.T.MAE.sort_values().index:
     df_each_pred[model] = df_wbm[each_true_col] + df_wbm[model] - df_wbm[e_form_col]
 
 
+# dataframe of all models' errors in their EACH predictions (eV/atom)
 df_each_err = pd.DataFrame()
 for model in df_metrics.T.MAE.sort_values().index:
     df_each_err[model] = df_wbm[model] - df_wbm[e_form_col]
diff --git a/site/src/app.html b/site/src/app.html
@@ -1,7 +1,6 @@
 <!DOCTYPE html>
 <html lang="en">
   <head>
-    <title>Matbench Discovery</title>
     <meta name="author" content="Janosh Riebesell" />
     <meta
       name="keywords"
@@ -11,10 +10,6 @@
     <meta name="og:card" content="summary" />
     <meta name="twitter:site" content="@jrib_" />
     <meta name="twitter:creator" content="@jrib_" />
-    <meta
-      name="description"
-      content="Benchmarking machine learning energy models for materials discovery."
-    />
 
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width,initial-scale=1" />

diff --git a/site/src/lib/Footer.svelte b/site/src/lib/Footer.svelte
@@ -6,7 +6,9 @@
 <footer>
   Questions/feedback?
   <a href="{repository}/issues"><Icon icon="octicon:mark-github" inline /></a>
-  <a href="mailto:janosh@lbl.gov"><Icon icon="mdi:email" inline /></a>
+  <a href="mailto:janosh@lbl.gov?subject=Matbench Discovery">
+    <Icon icon="mdi:email" inline />
+  </a>
 </footer>
 
 <style>

diff --git a/site/src/routes/+layout.svelte b/site/src/routes/+layout.svelte
@@ -18,8 +18,26 @@
   // get prev/next route with wrap-around
   $: next_route = routes[(current_route_idx + 1) % routes.length]
   $: prev_route = routes[(current_route_idx - 1 + routes.length) % routes.length]
+  $: description = {
+    '/': `Benchmarking machine learning energy models for materials discovery.`,
+    '/about-the-data': `Details about provenance, chemistry and energies in the benchmark's train and test set.`,
+    '/about-the-data/tmi': `Too much information on the benchmark's data.`,
+    '/api': `API docs for the Matbench Discovery PyPI package.`,
+    '/contribute': `Steps for contributing a new model to the benchmark.`,
+    '/models': `Details on each model sortable by metrics.`,
+    '/paper': `The paper released with the Matbench Discovery benchmark.`,
+    '/paper/iclr-ml4mat': `Extended abstract submitted to the ICLR ML4Materials workshop.`,
+    '/si': `Supplementary information including interesting but non-essential plots.`,
+  }[$page.route.id ?? ``]
+  if (!description) console.warn(`No description for route ${$page.route.id}`)
+  $: title = $page.route.id == `/` ? `` : `${$page.route.id} • `
 </script>
 
+<svelte:head>
+  <title>{title}Matbench Discovery</title>
+  <meta name="description" content={description} />
+</svelte:head>
+
 <Toc {headingSelector} breakpoint={1250} warnOnEmpty={false} />
 
 {#if $page.url.pathname !== `/`}

diff --git a/site/src/routes/api/+page.svelte b/site/src/routes/api/+page.svelte
@@ -14,6 +14,7 @@
     font-size: 25pt;
   }
   :global(hr) {
-    display: none;
+    border: 1px solid gray;
+    margin: 0 0 2em;
   }
 </style>
diff --git a/site/src/routes/contribute/+page.md b/site/src/routes/contribute/+page.md
@@ -170,7 +170,7 @@ git commit -m 'add <model_name> to Matbench Discovery leaderboard'
 
 And you're done! Once tests pass and the PR is merged, your model will be added to the leaderboard! 🎉
 
-### Step 4 (optional): Move WandB runs into our project
+### Step 4 (optional): Copy WandB runs into our project
 
 [Weights and Biases](https://wandb.ai) is a great tool for logging training and test runs of ML models. It's free, (partly) [open source](https://github.com/wandb/wandb) and offers a [special plan for academics](https://wandb.ai/site/research). It auto-collects metadata like
 

diff --git a/site/src/routes/paper/+layout.svelte b/site/src/routes/paper/+layout.svelte
@@ -57,10 +57,10 @@
   div :global(sup.ref) {
     text-transform: capitalize;
   }
-  /*auto-number HTML headings*/
+  /* auto-number HTML headings */
   div :global(h2) {
     counter-increment: h2;
-    counter-reset: h3, h4;
+    counter-reset: h3 h4;
   }
   div :global(h2::before) {
     content: counter(h2);

diff --git a/site/src/routes/paper/references.yaml b/site/src/routes/paper/references.yaml
@@ -1,5 +1,52 @@
 ---
 references:
+- id: allen_crystallographic_1999
+  abstract: >-
+    The development of strategies for the design of novel materials is
+    increasingly a knowledge-based activity and this book highlights a variety
+    of experimental and computational methods that provide structural knowledge
+    about individual compounds or groups of related compounds. However,
+    scientific research and development is an evolutionary process in which the
+    critical analysis and assessment of all existing information is an essential
+    component. In the area of chemical structure, such analyses provide
+    fundamental insights which can lead to the detection of trends and
+    probabilities, and to the formulation of hypotheses and rules. These
+    indications, in their turn, can direct the future course of structural
+    design, synthesis and experiment.
+  accessed:
+    - year: 2020
+      month: 8
+      day: 31
+  author:
+    - family: Allen
+      given: Frank H.
+    - family: Shields
+      given: Gregory P.
+  citation-key: allen_crystallographic_1999
+  collection-title: NATO Science Series
+  container-title: Implications of Molecular and Materials Structure for New Technologies
+  DOI: 10.1007/978-94-011-4653-1_21
+  editor:
+    - family: Howard
+      given: Judith A. K.
+    - family: Allen
+      given: Frank H.
+    - family: Shields
+      given: Gregory P.
+  event-place: Dordrecht
+  ISBN: 978-94-011-4653-1
+  issued:
+    - year: 1999
+  language: en
+  note: 'ZSCC: NoCitationData[s0]'
+  page: 291-302
+  publisher: Springer Netherlands
+  publisher-place: Dordrecht
+  source: Springer Link
+  title: Crystallographic Databases and Knowledge Bases in Materials Design
+  type: chapter
+  URL: https://doi.org/10.1007/978-94-011-4653-1_21
+
 - id: bartel_critical_2020
   abstract: >-
     Machine learning has emerged as a novel tool for the efficient prediction of
@@ -1021,6 +1068,57 @@ references:
   type: article
   URL: http://arxiv.org/abs/2206.08917
 
+- id: vaswani_attention_2017
+  abstract: >-
+    The dominant sequence transduction models are based on complex recurrent or
+    convolutional neural networks in an encoder-decoder configuration. The best
+    performing models also connect the encoder and decoder through an attention
+    mechanism. We propose a new simple network architecture, the Transformer,
+    based solely on attention mechanisms, dispensing with recurrence and
+    convolutions entirely. Experiments on two machine translation tasks show
+    these models to be superior in quality while being more parallelizable and
+    requiring significantly less time to train. Our model achieves 28.4 BLEU on
+    the WMT 2014 English-to-German translation task, improving over the existing
+    best results, including ensembles by over 2 BLEU. On the WMT 2014
+    English-to-French translation task, our model establishes a new single-model
+    state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight
+    GPUs, a small fraction of the training costs of the best models from the
+    literature. We show that the Transformer generalizes well to other tasks by
+    applying it successfully to English constituency parsing both with large and
+    limited training data.
+  accessed:
+    - year: 2020
+      month: 8
+      day: 5
+  author:
+    - family: Vaswani
+      given: Ashish
+    - family: Shazeer
+      given: Noam
+    - family: Parmar
+      given: Niki
+    - family: Uszkoreit
+      given: Jakob
+    - family: Jones
+      given: Llion
+    - family: Gomez
+      given: Aidan N.
+    - family: Kaiser
+      given: Lukasz
+    - family: Polosukhin
+      given: Illia
+  citation-key: vaswani_attention_2017
+  container-title: arXiv:1706.03762 [cs]
+  issued:
+    - year: 2017
+      month: 12
+      day: 5
+  note: 'ZSCC: 0010850'
+  source: arXiv.org
+  title: Attention Is All You Need
+  type: article-journal
+  URL: http://arxiv.org/abs/1706.03762
+
 - id: wang_predicting_2021
   abstract: >-
     We propose an efficient high-throughput scheme for the discovery of stable