deploy: 64afb93

lab-cosmo · Mar 26, 2024 · 8e3cabb · 8e3cabb
commit 8e3cabb
Show file tree

Hide file tree

Showing 81 changed files with 15,489 additions and 0 deletions.
diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,6 @@
+/.tox/
+/build/
+/dist/
+*.egg-info
+__pycache__/
+
diff --git a/.nojekyll b/.nojekyll
diff --git a/index.html b/index.html
@@ -0,0 +1,11 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+  <meta charset="utf-8" />
+  <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" />
+  <meta http-equiv="refresh" content="0;URL=latest/index.html" />
+</head>
+
+<body></body>
+</html>
diff --git a/latest/.buildinfo b/latest/.buildinfo
@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: d81abe5bca4711c0a089df227c9a417f
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/latest/.doctrees/environment.pickle b/latest/.doctrees/environment.pickle
diff --git a/latest/.doctrees/examples/gaas-map/gaas-map.doctree b/latest/.doctrees/examples/gaas-map/gaas-map.doctree
diff --git a/latest/.doctrees/examples/gaas-map/sg_execution_times.doctree b/latest/.doctrees/examples/gaas-map/sg_execution_times.doctree
diff --git a/latest/.doctrees/examples/lode-linear/lode-linear.doctree b/latest/.doctrees/examples/lode-linear/lode-linear.doctree
diff --git a/latest/.doctrees/examples/lode-linear/sg_execution_times.doctree b/latest/.doctrees/examples/lode-linear/sg_execution_times.doctree
diff --git a/latest/.doctrees/examples/roy-gch/roy-gch.doctree b/latest/.doctrees/examples/roy-gch/roy-gch.doctree
diff --git a/latest/.doctrees/examples/roy-gch/sg_execution_times.doctree b/latest/.doctrees/examples/roy-gch/sg_execution_times.doctree
diff --git a/latest/.doctrees/examples/sample-selection/sample-selection.doctree b/latest/.doctrees/examples/sample-selection/sample-selection.doctree
diff --git a/latest/.doctrees/examples/sample-selection/sg_execution_times.doctree b/latest/.doctrees/examples/sample-selection/sg_execution_times.doctree
diff --git a/latest/.doctrees/index.doctree b/latest/.doctrees/index.doctree
diff --git a/latest/_downloads/372c6744f93b866ccf802a12006619bb/roy-gch.py b/latest/_downloads/372c6744f93b866ccf802a12006619bb/roy-gch.py
@@ -0,0 +1,292 @@
+"""
+Generalized Convex Hull construction for the polymorphs of ROY
+==============================================================
+
+This notebook analyzes the structures of 264 polymorphs of ROY, from
+`Beran et Al, Chemical Science
+(2022) <https://doi.org/10.1039/D1SC06074K>`__, comparing the
+conventional density-energy convex hull with a Generalized Convex Hull
+(GCH) analysis (see `Anelli et al., Phys. Rev. Materials
+(2018) <https://doi.org/10.1103/PhysRevMaterials.2.103804>`__).
+It uses features computed with `rascaline <https://github.com/lab-cosmo/rascaline>`__
+and uses the directional convex hull function from
+`scikit-matter <https://github.com/lab-cosmo/scikit-matter>`__
+to make the figure.
+"""
+
+import chemiscope
+import matplotlib.tri
+import numpy as np
+from matplotlib import pyplot as plt
+from metatensor import mean_over_samples
+from rascaline import SoapPowerSpectrum
+from sklearn.decomposition import PCA
+from skmatter.datasets import load_roy_dataset
+from skmatter.sample_selection import DirectionalConvexHull
+
+
+# %%
+# Loads the structures (that also contain properties in the ``info`` field)
+
+roy_data = load_roy_dataset()
+
+structures = roy_data["structures"]
+
+density = np.array([s.info["density"] for s in structures])
+energy = np.array([s.info["energy"] for s in structures])
+structype = np.array([s.info["type"] for s in structures])
+iknown = np.where(structype == "known")[0]
+iothers = np.where(structype != "known")[0]
+
+
+# %%
+# Energy-density hull
+# -------------------
+#
+# The Directional Convex Hull routines can be used to compute a
+# conventional density-energy hull
+
+dch_builder = DirectionalConvexHull(low_dim_idx=[0])
+dch_builder.fit(density.reshape(-1, 1), energy)
+
+# %%
+# We can get the indices of the selection, and compute the distance from
+# the hull
+
+sel = dch_builder.selected_idx_
+dch_dist = dch_builder.score_samples(density.reshape(-1, 1), energy)
+
+
+# %%
+#
+# Hull energies
+# ^^^^^^^^^^^^^
+#
+# Structures on the hull are stable with respect to synthesis at constant
+# molar volume. Any other structure would lower the energy by decomposing
+# into a mixture of the two nearest structures along the hull. Given that
+# the lattice energy is an imperfect proxy for the free energy, and that
+# synthesis can be performed in other ways than by fixing the density,
+# structures that are not exactly on the hull might also be stable. One
+# can compute a “hull energy” as an indication of how close these
+# structures are to being stable.
+
+fig, ax = plt.subplots(1, 1, figsize=(6, 4))
+ax.scatter(density, energy, c=dch_dist, marker=".")
+ssel = sel[np.argsort(density[sel])]
+ax.plot(density[ssel], energy[ssel], "k--")
+ax.set_xlabel("density / g/cm$^3$")
+ax.set_ylabel("energy / kJ/mol")
+
+print(
+    f"Mean hull energy for 'known' stable structures {dch_dist[iknown].mean()} kJ/mol"
+)
+print(f"Mean hull energy for 'other' structures {dch_dist[iothers].mean()} kJ/mol")
+
+
+# %%
+# Interactive visualization
+# ^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# You can also visualize the hull with ``chemiscope``.
+# This runs only in a notebook, and
+# requires having the ``chemiscope`` package installed.
+#
+
+cs = chemiscope.show(
+    structures,
+    dict(
+        energy=energy,
+        density=density,
+        hull_energy=dch_dist,
+        structure_type=structype,
+    ),
+    settings={
+        "map": {
+            "x": {"property": "density"},
+            "y": {"property": "energy"},
+            "color": {"property": "hull_energy"},
+            "symbol": "structure_type",
+            "size": {"factor": 35},
+        },
+        "structure": [{"unitCell": True, "supercell": {"0": 2, "1": 2, "2": 2}}],
+    },
+)
+
+
+if chemiscope.jupyter._is_running_in_notebook():
+    from IPython.display import display
+
+    display(cs)
+else:
+    cs.save("roy_ch.json.gz")
+
+# %%
+# Generalized Convex Hull
+# -----------------------
+#
+# A GCH is a similar construction, in which generic structural descriptors
+# are used in lieu of composition, density or other thermodynamic
+# constraints. The idea is that configurations that are found close to the
+# GCH are locally stable with respect to structurally-similar
+# configurations. In other terms, one can hope to find a thermodynamic
+# constraint (i.e. synthesis conditions) that act differently on these
+# structures in comparison with the others, and may potentially stabilize
+# them.
+#
+
+
+# %%
+# Compute structural descriptors
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# A first step is to computes suitable ML descriptors. Here we have used
+# ``rascaline`` to evaluate average SOAP features for the structures.
+# If you don't want to install these dependencies for this example you
+# can also use the pre-computed features, but you can use this as a stub
+# to apply this analysis to other chemical systems
+
+hypers = {
+    "cutoff": 4,
+    "max_radial": 6,
+    "max_angular": 4,
+    "atomic_gaussian_width": 0.7,
+    "cutoff_function": {"ShiftedCosine": {"width": 0.5}},
+    "radial_basis": {"Gto": {"accuracy": 1e-6}},
+    "center_atom_weight": 1.0,
+}
+calculator = SoapPowerSpectrum(**hypers)
+rho2i = calculator.compute(structures)
+rho2i = rho2i.keys_to_samples(["species_center"]).keys_to_properties(
+    ["species_neighbor_1", "species_neighbor_2"]
+)
+rho2i_structure = mean_over_samples(rho2i, sample_names=["center", "species_center"])
+np.savez("roy_features.npz", feats=rho2i_structure.block(0).values)
+
+
+# features = roy_data["features"]
+features = rho2i_structure.block(0).values
+
+
+# %%
+# PCA projection
+# ^^^^^^^^^^^^^^
+#
+# Computes PCA projection to generate low-dimensional descriptors that
+# reflect structural diversity. Any other dimensionality reduction scheme
+# could be used in a similar fashion.
+
+pca = PCA(n_components=4)
+pca_features = pca.fit_transform(features)
+
+fig, ax = plt.subplots(1, 1, figsize=(6, 4))
+scatter = ax.scatter(pca_features[:, 0], pca_features[:, 1], c=energy)
+ax.set_xlabel("PCA[1]")
+ax.set_ylabel("PCA[2]")
+cbar = fig.colorbar(scatter, ax=ax)
+cbar.set_label("energy / kJ/mol")
+
+
+# %%
+# Builds the Generalized Convex Hull
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Builds a convex hull on the first two PCA features
+
+dch_builder = DirectionalConvexHull(low_dim_idx=[0, 1])
+dch_builder.fit(pca_features, energy)
+sel = dch_builder.selected_idx_
+dch_dist = dch_builder.score_samples(pca_features, energy)
+
+
+# %%
+# Generates a 3D Plot
+#
+
+triang = matplotlib.tri.Triangulation(pca_features[sel, 0], pca_features[sel, 1])
+fig = plt.figure(figsize=(7, 5), tight_layout=True)
+ax = fig.add_subplot(projection="3d")
+ax.plot_trisurf(triang, energy[sel], color="gray")
+ax.scatter(pca_features[:, 0], pca_features[:, 1], energy, c=dch_dist)
+ax.set_xlabel("PCA[1]")
+ax.set_ylabel("PCA[2]")
+ax.set_zlabel("energy / kJ/mol\n \n", labelpad=11)
+ax.view_init(25, 110)
+
+
+# %%
+# The GCH construction improves the separation between the hull energies
+# of “known” and hypothetical polymorphs (compare with the density-energy
+# values above)
+
+print(
+    f"Mean hull energy for 'known' stable structures {dch_dist[iknown].mean()} kJ/mol"
+)
+print(f"Mean hull energy for 'other' structures {dch_dist[iothers].mean()} kJ/mol")
+
+
+# %%
+# Visualize in ``chemiscope``. This runs only in a notebook, and
+# requires having the ``chemiscope`` package installed.
+
+for i, f in enumerate(structures):
+    for j in range(len(pca_features[i])):
+        f.info["pca_" + str(j + 1)] = pca_features[i, j]
+structure_properties = chemiscope.extract_properties(structures)
+structure_properties.update({"per_atom_energy": energy, "hull_energy": dch_dist})
+
+# shows chemiscope if not run in terminal
+
+cs = chemiscope.show(
+    frames=structures,
+    properties=structure_properties,
+    meta={
+        "name": "GCH for ROY polymorphs",
+        "description": """
+Demonstration of the Generalized Convex Hull construction for
+polymorphs of the ROY molecule. Molecules that are closest to
+the hull built on PCA-based structural descriptors and having the
+internal energy predicted by electronic-structure calculations as
+the z axis are the most thermodynamically stable. Indeed most of the
+known polymorphs of ROY are on (or very close) to this hull.
+""",
+        "authors": ["Michele Ceriotti <[email protected]>"],
+        "references": [
+            'A. Anelli, E. A. Engel, C. J. Pickard, and M. Ceriotti, \
+            "Generalized convex hull construction for materials discovery," \
+            Physical Review Materials 2(10), 103804 (2018).',
+            'G. J. O. Beran, I. J. Sugden, C. Greenwell, D. H. Bowskill, \
+            C. C. Pantelides, and C. S. Adjiman, "How many more polymorphs of \
+            ROY remain undiscovered," Chem. Sci. 13(5), 1288–1297 (2022).',
+        ],
+    },
+    settings={
+        "map": {
+            "x": {"property": "pca_1"},
+            "y": {"property": "pca_2"},
+            "z": {"property": "energy"},
+            "symbol": "type",
+            "color": {"property": "hull_energy"},
+            "size": {
+                "factor": 35,
+                "mode": "linear",
+                "property": "",
+                "reverse": True,
+            },
+        },
+        "structure": [
+            {
+                "bonds": True,
+                "unitCell": True,
+                "keepOrientation": True,
+            }
+        ],
+    },
+)
+
+if chemiscope.jupyter._is_running_in_notebook():
+    from IPython.display import display
+
+    display(cs)
+else:
+    cs.save("roy_gch.json.gz")