add color bar labels to MP/WBM/MPtrj ptable element occurrence heatmaps

tweak plot scripts
update site deps, esp. elementari to fix black text on ptable element tiles missing data

.pre-commit-config.yaml

@@ -7,7 +7,7 @@ default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.9
+    rev: v0.1.13
     hooks:
       - id: ruff
         args: [--fix]
@@ -56,7 +56,7 @@ repos:
         exclude: ^(site/src/figs/.+\.svelte|data/wbm/20.+\..+|site/src/routes/.+\.(yaml|json)|changelog.md)$
 
   - repo: https://github.com/pre-commit/mirrors-eslint
-    rev: v8.56.0
+    rev: v9.0.0-alpha.0
     hooks:
       - id: eslint
         types: [file]

data/wbm/eda_wbm.py

@@ -14,20 +14,21 @@
     spacegroup_sunburst,
 )
 from pymatviz.io import save_fig
-from pymatviz.utils import si_fmt
+from pymatviz.utils import si_fmt, si_fmt_int
 
 from matbench_discovery import (
     PDF_FIGS,
     ROOT,
     SITE_FIGS,
     STABILITY_THRESHOLD,
+    e_form_raw_col,
     formula_col,
     id_col,
 )
 from matbench_discovery import plots as plots
 from matbench_discovery.data import DATA_FILES, df_wbm
 from matbench_discovery.energy import mp_elem_reference_entries
-from matbench_discovery.preds import df_each_err, each_true_col
+from matbench_discovery.preds import df_each_err, e_form_col, each_true_col
 
 __author__ = "Janosh Riebesell"
 __date__ = "2023-03-30"
@@ -141,8 +142,8 @@
 
 # %% histogram of energy distance to MP convex hull for WBM
 e_col = each_true_col  # or e_form_col
-e_col = "e_form_per_atom_uncorrected"
-e_col = "e_form_per_atom_mp2020_corrected"
+# e_col = e_form_raw_col
+# e_col = e_form_col
 mean, std = df_wbm[e_col].mean(), df_wbm[e_col].std()
 
 range_x = (mean - 2 * std, mean + 2 * std)
@@ -170,9 +171,28 @@
     dummy_mae = (df_wbm[e_col] - df_wbm[e_col].mean()).abs().mean()
 
     title = (
-        f"{len(df_wbm.dropna()):,} structures with {n_stable:,} stable + {n_unstable:,}"
+        f"{si_fmt_int(len(df_wbm.dropna()))} structures with {si_fmt_int(n_stable)} "
+        f"stable + {si_fmt_int(n_unstable)} unstable (stable rate="
+        f"{n_stable / len(df_wbm):.1%})"
     )
-    fig.layout.title = dict(text=title, x=0.5)
+    fig.layout.title = dict(text=title, x=0.5, font_size=16, y=0.95)
+
+    # add red/blue annotations to left and right of mean saying stable/unstable
+    for idx, (label, x_pos) in enumerate(
+        (("stable", mean - std), ("unstable", mean + std))
+    ):
+        fig.add_annotation(
+            x=x_pos,
+            y=0.5,
+            text=label,
+            showarrow=False,
+            font_size=18,
+            font_color=px.colors.qualitative.Plotly[idx],
+            yref="paper",
+            xanchor="right",
+            xshift=-40,
+        )
+
 
 fig.layout.margin = dict(l=0, r=0, b=0, t=40)
 fig.update_layout(showlegend=False)
@@ -183,14 +203,19 @@
     (mean + std, f"{mean + std = :.2f}"),
 ):
     anno = dict(text=label, yshift=-10, xshift=-5, xanchor="right")
-    line_width = 1 if x_pos == mean else 0.5
+    line_width = 3 if x_pos == mean else 2
     fig.add_vline(x=x_pos, line=dict(width=line_width, dash="dash"), annotation=anno)
 
 fig.show()
-
-save_fig(fig, f"{SITE_FIGS}/hist-wbm-hull-dist.svelte")
-# save_fig(fig, "./figs/hist-wbm-hull-dist.svg", width=1000, height=500)
-save_fig(fig, f"{PDF_FIGS}/hist-wbm-hull-dist.pdf")
+suffix = {
+    each_true_col: "hull-dist",
+    e_form_col: "e-form",
+    e_form_raw_col: "e-form-uncorrected",
+}[e_col]
+img_name = f"hist-wbm-{suffix}"
+save_fig(fig, f"{SITE_FIGS}/{img_name}.svelte")
+# save_fig(fig, f"./figs/{img_name}.svg", width=800, height=500)
+save_fig(fig, f"{PDF_FIGS}/{img_name}.pdf", width=600, height=300)
 
 
 # %%

data/wbm/fetch_process_wbm_dataset.py

@@ -19,7 +19,14 @@
 from pymatviz.io import save_fig
 from tqdm import tqdm
 
-from matbench_discovery import PDF_FIGS, SITE_FIGS, formula_col, id_col, today
+from matbench_discovery import (
+    PDF_FIGS,
+    SITE_FIGS,
+    e_form_raw_col,
+    formula_col,
+    id_col,
+    today,
+)
 from matbench_discovery.data import DATA_FILES
 from matbench_discovery.energy import get_e_form_per_atom
 
@@ -39,7 +46,7 @@
 
 
 module_dir = os.path.dirname(__file__)
-e_form_col = "e_form_per_atom_wbm"
+e_form_wbm_col = "e_form_per_atom_wbm"
 
 
 # %% links to google drive files received via email from 1st author Hai-Chen Wang
@@ -296,7 +303,7 @@ def increment_wbm_material_id(wbm_id: str) -> str:
     "nsites": "n_sites",
     "vol": "volume",
     "e": "uncorrected_energy",
-    "e_form": e_form_col,
+    "e_form": e_form_wbm_col,
     "e_hull": "e_above_hull_wbm",
     "gap": "bandgap_pbe",
     "id": id_col,
@@ -440,15 +447,15 @@ def fix_bad_struct_index_mismatch(material_id: str) -> str:
 
 # %% remove suspicious formation energy outliers
 e_form_cutoff = 5
-n_too_stable = sum(df_summary[e_form_col] < -e_form_cutoff)
+n_too_stable = sum(df_summary[e_form_wbm_col] < -e_form_cutoff)
 print(f"{n_too_stable = }")  # n_too_stable = 502
-n_too_unstable = sum(df_summary[e_form_col] > e_form_cutoff)
+n_too_unstable = sum(df_summary[e_form_wbm_col] > e_form_cutoff)
 print(f"{n_too_unstable = }")  # n_too_unstable = 22
 
 e_form_hist, e_form_bins = np.histogram(
-    df_summary[e_form_col], bins=300, range=(-5.5, 5.5)
+    df_summary[e_form_wbm_col], bins=300, range=(-5.5, 5.5)
 )
-x_label = {e_form_col: "WBM uncorrected formation energy (eV/atom)"}[e_form_col]
+x_label = {e_form_wbm_col: "WBM uncorrected formation energy (eV/atom)"}[e_form_wbm_col]
 fig = px.bar(
     x=e_form_bins[:-1],  # [:-1] to drop last bin edge which is not needed
     y=e_form_hist,
@@ -485,7 +492,7 @@ def fix_bad_struct_index_mismatch(material_id: str) -> str:
 # %%
 assert len(df_summary) == len(df_wbm) == 257_487
 
-query_str = f"{-e_form_cutoff} < {e_form_col} < {e_form_cutoff}"
+query_str = f"{-e_form_cutoff} < {e_form_wbm_col} < {e_form_cutoff}"
 dropped_ids = sorted(set(df_summary.index) - set(df_summary.query(query_str).index))
 assert len(dropped_ids) == 502 + 22
 assert dropped_ids[:3] == "wbm-1-12142 wbm-1-12143 wbm-1-12144".split()
@@ -569,8 +576,6 @@ def fix_bad_struct_index_mismatch(material_id: str) -> str:
 # first make sure source and target dfs have matching indices
 assert sum(df_wbm.index != df_summary.index) == 0
 
-e_form_col = "e_form_per_atom_uncorrected"
-
 for row in tqdm(df_wbm.itertuples(), total=len(df_wbm)):
     mat_id, cse, formula = row.Index, row.cse, row.formula_from_cse
     assert mat_id == cse.entry_id, f"{mat_id=} != {cse.entry_id=}"
@@ -585,11 +590,11 @@ def fix_bad_struct_index_mismatch(material_id: str) -> str:
     assert (
         abs(e_form - e_form_ppd) < 1e-4
     ), f"{mat_id}: {e_form=:.3} != {e_form_ppd=:.3} (diff={e_form - e_form_ppd:.3}))"
-    df_summary.loc[cse.entry_id, e_form_col] = e_form
+    df_summary.loc[cse.entry_id, e_form_raw_col] = e_form
 
 
-df_summary[e_form_col.replace("uncorrected", "mp2020_corrected")] = (
-    df_summary[e_form_col] + df_summary["e_correction_per_atom_mp2020"]
+df_summary[e_form_raw_col.replace("uncorrected", "mp2020_corrected")] = (
+    df_summary[e_form_raw_col] + df_summary["e_correction_per_atom_mp2020"]
 )
 
 

data/wbm/readme.md

@@ -90,18 +90,18 @@ The number of stable materials (according to the MP convex hull which is spanned
 
 The WBM test set and even more so the MP training set are heavily oxide dominated. The WBM test set is about 75% larger than the MP training set and also more chemically diverse, containing a higher fraction of transition metals, post-transition metals and metalloids. Our goal in picking such a large diverse test set is future-proofing. Ideally, this data will provide a challenging materials discovery test bed even for large foundational ML models in the future.
 
-Below: Element counts for WBM test set consisting of 256,963 WBM `ComputedStructureEntries`
-
 <slot name="wbm-elements-heatmap">
   <img src="./figs/wbm-elements.svg" alt="Periodic table log heatmap of WBM elements">
 </slot>
 
-Below: Element counts for MP training set consisting of 154,719 `ComputedStructureEntries`
+The WBM test set consists of 256,963 WBM `ComputedStructureEntries`
 
 <slot name="mp-elements-heatmap">
   <img src="./figs/mp-elements.svg" alt="Periodic table log heatmap of MP elements">
 </slot>
 
+The MP training set consists of 154,719 `ComputedStructureEntries`
+
 <slot name="mp-trj-elements-heatmap" />
 
 ## 📊 &thinsp; Symmetry Statistics

matbench_discovery/__init__.py

@@ -44,6 +44,7 @@
 init_struct_col = "initial_structure"
 struct_col = "structure"
 e_form_col = "formation_energy_per_atom"
+e_form_raw_col = "e_form_per_atom_uncorrected"
 formula_col = "formula"
 stress_col = "stress"
 stress_trace_col = "stress_trace"

matbench_discovery/plots.py

@@ -483,6 +483,8 @@ def rolling_mae_vs_hull_dist(
             y=(1, dft_acc, dft_acc, 1) if show_dft_acc else (1, 0, 1),
             name=triangle_anno,
             fillcolor="red",
+            # remove triangle border
+            line=dict(color="rgba(0,0,0,0)"),
             **scatter_kwds,
         )
         fig.add_annotation(
@@ -535,14 +537,10 @@ def rolling_mae_vs_hull_dist(
 
         from matbench_discovery.preds import model_styles
 
-        for idx, trace in enumerate(fig.data):
+        for trace in fig.data:
             if style := model_styles.get(trace.name):
                 ls, _marker, color = style
                 trace.line = dict(color=color, dash=ls, width=2)
-            else:
-                trace.line = dict(
-                    color=plotly_colors[idx], dash=plotly_line_styles[idx], width=3
-                )
             # marker_spacing = 2
             # fig.add_scatter(
             #     x=trace.x[::marker_spacing],

matbench_discovery/preds.py

@@ -30,9 +30,9 @@
 each_pred_col = "e_above_hull_pred"
 model_mean_each_col = "Mean prediction all models"
 model_mean_err_col = "Mean error all models"
-model_std_col = "Std. dev. over models"
+model_std_each_col = "Std. dev. over models"
 
-for col in (model_mean_each_col, model_mean_err_col, model_std_col):
+for col in (model_mean_each_col, model_mean_err_col, model_std_each_col):
     quantity_labels[col] = f"{col} {ev_per_atom}"
 
 
@@ -211,7 +211,7 @@ def load_df_wbm_with_preds(
     )
 
 # important: do df_each_pred.std(axis=1) before inserting model_mean_each_col into df
-df_preds[model_std_col] = df_each_pred.std(axis=1)
+df_preds[model_std_each_col] = df_each_pred.std(axis=1)
 df_each_pred[model_mean_each_col] = df_preds[model_mean_each_col] = df_each_pred.mean(
     axis=1
 )

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 name = "matbench-discovery"
 version = "1.0.0"
 description = "A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures"
-authors = [{ name = "Janosh Riebesell", email = "janosh@lbl.gov" }]
+authors = [{ name = "Janosh Riebesell", email = "janosh.riebesell@gmail.com" }]
 readme = "readme.md"
 license = { file = "license" }
 keywords = [
@@ -60,7 +60,7 @@ running-models = [
 
   "aviary@git+https://github.com/CompRhys/aviary",
   "m3gnet",
-  "mace@git+https://github.com/ACEsuit/mace",
+  "mace-torch",
   "maml",
   "megnet",
 ]

readme.md

@@ -5,7 +5,7 @@
 
 <h4 align="center" class="toc-exclude">
 
-[![arXiv](https://img.shields.io/badge/arXiv-2308.14920-blue)](https://arxiv.org/abs/2308.14920)
+[![arXiv](https://img.shields.io/badge/arXiv-2308.14920-blue?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2308.14920)
 [![Tests](https://github.com/janosh/matbench-discovery/actions/workflows/test.yml/badge.svg)](https://github.com/janosh/matbench-discovery/actions/workflows/test.yml)
 [![GitHub Pages](https://github.com/janosh/matbench-discovery/actions/workflows/gh-pages.yml/badge.svg)](https://github.com/janosh/matbench-discovery/actions/workflows/gh-pages.yml)
 [![Requires Python 3.9+](https://img.shields.io/badge/Python-3.9+-blue.svg?logo=python&logoColor=white)](https://python.org/downloads)

scripts/model_figs/analyze_model_disagreement.py

@@ -18,7 +18,7 @@
     each_true_col,
     model_mean_each_col,
     model_mean_err_col,
-    model_std_col,
+    model_std_each_col,
 )
 
 __author__ = "Janosh Riebesell"
@@ -56,8 +56,7 @@
     fig = df_plot.plot.scatter(
         x=each_true_col,
         y=model_mean_each_col,
-        color=model_std_col,
-        size="n_sites",
+        color=model_std_each_col,
         backend="plotly",
         hover_name=id_col,
         hover_data=[formula_col],
@@ -71,11 +70,14 @@
     fig.layout.coloraxis.colorbar.update(title_side="right", thickness=14)
     fig.layout.margin.update(l=0, r=30, b=0, t=60)
     add_identity_line(fig)
+    label = {"all": "structures"}.get(material_cls, material_cls)
     fig.layout.title.update(
-        text=f"{n_structs} largest {material_cls} model errors: Predicted vs.<br>"
-        "DFT hull distance colored by model disagreement",
+        text=f"{n_structs} {material_cls} with largest hull distance errors<br>"
+        "colored by model disagreement, sized by number of sites",
         x=0.5,
     )
+    # size markers by structure
+    fig.data[0].marker.size = df_plot["n_sites"] ** 0.5 * 3
     # tried setting error_y=model_std_col but looks bad
     # fig.update_traces(
     #     error_y=dict(color="rgba(255,255,255,0.2)", width=3, thickness=2)

scripts/model_figs/parity_energy_models.py

@@ -60,7 +60,7 @@
     df_melt,
     bin_by_cols=[e_true_col, e_pred_col],
     group_by_cols=[facet_col],
-    n_bins=200,
+    n_bins=300,
     bin_counts_col=(bin_cnt_col := "bin counts"),
 )
 df_bin = df_bin.reset_index()
@@ -162,7 +162,8 @@
     # pick from https://plotly.com/python/builtin-colorscales
     color_continuous_scale="agsunset",
 )
-
+# decrease marker size
+fig.update_traces(marker=dict(size=2))
 # manually set colorbar ticks and labels (needed after log1p transform)
 tick_vals = [1, 10, 100, 1000, 10_000]
 fig.layout.coloraxis.colorbar.update(
@@ -181,9 +182,8 @@
     assert model in df_preds, f"Unexpected {model=} not in {list(df_preds)=}"
     # add MAE and R2 to subplot titles
     MAE, R2 = df_metrics[model][["MAE", "R2"]]
-    fig.layout.annotations[
-        idx - 1
-    ].text = f"{model} · {MAE=:.2f} · R<sup>2</sup>={R2:.2f}"
+    sub_title = f"{model} · {MAE=:.2f} · R<sup>2</sup>={R2:.2f}"
+    fig.layout.annotations[idx - 1].text = sub_title
 
     # remove subplot x and y axis titles
     fig.layout[f"xaxis{idx}"].title.text = ""
@@ -222,7 +222,7 @@
             yshift=-15 * sign_y,
             text=label,
             showarrow=False,
-            font=dict(size=16, color=color),
+            font=dict(size=14, color=color),
             row="all",
             col="all",
         )
@@ -245,9 +245,10 @@
 # fig.update_layout(yaxis=dict(scaleanchor="x", scaleratio=1))
 
 axis_titles = dict(xref="paper", yref="paper", showarrow=False)
+portrait = n_rows > n_cols
 fig.add_annotation(  # x-axis title
     x=0.5,
-    y=-0.06,
+    y=-0.06 if portrait else -0.18,
     text=x_title,
     **axis_titles,
 )
@@ -259,10 +260,10 @@
     **axis_titles,
 )
 
-fig.layout.height = 230 * n_rows
+fig.layout.update(height=230 * n_rows, width=180 * n_cols)
 fig.layout.coloraxis.colorbar.update(orientation="h", thickness=9, len=0.5, y=1.05)
 # fig.layout.width = 1100
-fig.layout.margin.update(l=40, r=10, t=30, b=60)
+fig.layout.margin.update(l=40, r=10, t=30 if portrait else 10, b=60 if portrait else 10)
 fig.update_xaxes(matches=None)
 fig.update_yaxes(matches=None)
 fig.show()