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5 changes: 5 additions & 0 deletions other/materials_designer/workflows/requirements-with-made.txt
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numpy<2.0
scipy>=1.5.4
matplotlib>=3.0.0
ase>=3.22.1
mat3ra-made[tools]>=2024.11.12.post0
193 changes: 193 additions & 0 deletions other/materials_designer/workflows/vbo_polar.pyi
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# ------------------------------------------------------------------ #
# Linear Fit of ESP for Polar Interface VBO Calculation #
# ------------------------------------------------------------------ #
#
# Reference: Choudhary & Garrity, arXiv:2401.02021 (InterMat) #
# #
# For polar interfaces, ESP shows linear gradient in bulk regions #
# due to internal electric field. We fit each slab region and use #
# the average value of the fit as the ESP reference. #
# #
# VBO Calculation: #
# 1. Fit interface profile over slab 1 region → Va_interface #
# 2. Fit interface profile over slab 2 region → Vb_interface #
# 3. Fit bulk left profile over slab 1 region → Va_bulk_left #
# 4. Fit bulk right profile over slab 2 region → Vb_bulk_right #
# 5. VBO = (∆V_interface) - (∆V_bulk) #
# where ∆V_interface = Vb_interface - Va_interface #
# ∆V_bulk = Vb_bulk_right - Va_bulk_left #
# #
# Input: #
# - profile_left, profile_right: ESP profiles for bulk materials #
# - profile_interface: ESP profile for interface structure #
# #
# Output: VBO (Valence Band Offset) #
# #
# NEW: Slab boundaries auto-detected using fingerprint matching #
# ------------------------------------------------------------------ #
import json

import matplotlib
import ase.io
from mat3ra.made.material import Material
from mat3ra.made.tools.convert import from_ase

# Non-interactive backend for running the script on the server, if working in Jupyter, comment out the next line
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from types import SimpleNamespace
from scipy.stats import linregress

# Read structure from pw_scf.out (generated by previous pw_scf calculation)
# Material index: 0=Interface, 1=Left, 2=Right
# Files are named: pw_scf.out, pw_scf.out-1, pw_scf.out-2
pw_scf_output = f"./pw_scf.out"
pw_scf_output_1 = f"./pw_scf.out-1"
pw_scf_output_2 = f"./pw_scf.out-2"

# Read atomic structure from espresso output
atoms = ase.io.read(pw_scf_output, format="espresso-out")
atoms_1 = ase.io.read(pw_scf_output_1, format="espresso-out")
atoms_2 = ase.io.read(pw_scf_output_2, format="espresso-out")

# Convert ASE Atoms to Material using mat3ra-made
material = Material.create(from_ase(atoms))
material_1 = Material.create(from_ase(atoms_1))
material_2 = Material.create(from_ase(atoms_2))

material.to_cartesian()
material_1.to_cartesian()
material_2.to_cartesian()

# Get the z-coordinate boundaries of each slab using element-based matching
coords = material.basis.coordinates.values
elements = material.basis.elements.values
z_elements = sorted(zip([c[2] for c in coords], elements))
n_left = len(material_1.basis.elements.values)

z_max_1 = z_elements[n_left - 1][0] # Last atom of left slab
z_min_2 = z_elements[n_left][0] # First atom of right slab
z_min_1 = z_elements[0][0]
z_max_2 = z_elements[-1][0]

print(f"Detected Slab 1 (left) boundaries: z = {z_min_1:.3f} to {z_max_1:.3f} Å")
print(f"Detected Slab 2 (right) boundaries: z = {z_min_2:.3f} to {z_max_2:.3f} Å")

# Data from context: macroscopic average potential profile
CHECKPOINT_FILE = "./.mat3ra/checkpoint.json"
with open(CHECKPOINT_FILE, "r") as f:
checkpoint_data = json.load(f)
profile_interface = SimpleNamespace(
**checkpoint_data["scope"]["local"]["average-electrostatic-potential"]["average_potential_profile"]
)
profile_left = SimpleNamespace(
**checkpoint_data["scope"]["local"]["average-electrostatic-potential-left"]["average_potential_profile"]
)
profile_right = SimpleNamespace(
**checkpoint_data["scope"]["local"]["average-electrostatic-potential-right"]["average_potential_profile"]
)

# Interface ESP profile
X = np.array(profile_interface.xDataArray) # z-coordinates (angstrom)
Y = np.array(profile_interface.yDataSeries[1]) # Macroscopic average V̄(z)

# Bulk material ESP profiles
X_left = np.array(profile_left.xDataArray)
Y_left = np.array(profile_left.yDataSeries[1])
X_right = np.array(profile_right.xDataArray)
Y_right = np.array(profile_right.yDataSeries[1])

def get_region_indices(x_data, x_min, x_max):
"""Get array indices corresponding to coordinate range."""
mask = (x_data >= x_min) & (x_data <= x_max)
indices = np.where(mask)[0]
if len(indices) == 0:
return 0, len(x_data)
return indices[0], indices[-1] + 1
Comment on lines +101 to +107
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⚠️ Potential issue | 🟠 Major

Don’t fit the entire profile when a region is empty.

Returning (0, len(x_data)) on empty regions silently fits the full profile and produces incorrect Va/Vb when the region bounds are wrong. Fail fast (or explicitly return empty) instead.

🐛 Proposed fix (fail fast on empty regions)
 def get_region_indices(x_data, x_min, x_max):
     """Get array indices corresponding to coordinate range."""
     mask = (x_data >= x_min) & (x_data <= x_max)
     indices = np.where(mask)[0]
     if len(indices) == 0:
-        return 0, len(x_data)
+        return 0, 0
     return indices[0], indices[-1] + 1

 # Get indices for each slab region
 slab1_start, slab1_end = get_region_indices(X, z_min_1, z_max_1)
 slab2_start, slab2_end = get_region_indices(X, z_min_2, z_max_2)
+
+if slab1_end <= slab1_start:
+    raise ValueError(f"No points found in slab 1 region ({z_min_1}, {z_max_1}).")
+if slab2_end <= slab2_start:
+    raise ValueError(f"No points found in slab 2 region ({z_min_2}, {z_max_2}).")

Also applies to: 94-100

🤖 Prompt for AI Agents
In `@other/materials_designer/workflows/vbo_polar.pyi` around lines 61 - 67, The
helper get_region_indices currently returns (0, len(x_data)) when no points fall
in [x_min, x_max], which silently causes full-profile fits; change
get_region_indices to fail fast by raising a ValueError with a clear message
including x_min and x_max (e.g., "empty region for x_min=..., x_max=...")
instead of returning a full-range sentinel; apply the same change to the other
identical implementation around lines 94-100 so callers cannot accidentally fit
the entire profile when a region is empty.


def fit_and_average(x_data, y_data, start_idx, end_idx):
"""
Fit linear regression to region and return average value, slope, and intercept.
The average of the fitted line equals the mean of y-values,
but fitting helps smooth out oscillations and validates linearity.
"""
x_region = x_data[start_idx:end_idx]
y_region = y_data[start_idx:end_idx]

if len(x_region) < 2:
avg = float(np.mean(y_region)) if len(y_region) > 0 else 0.0
return avg, 0.0, avg
Comment on lines +109 to +121
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⚠️ Potential issue | 🟠 Major

Avoid silent 0.0 when the fit region is empty.

Returning 0.0 for an empty region can silently skew the VBO. Consider raising a ValueError on empty regions and only using the “avg with zero slope” fallback for exactly one point.

🐛 Proposed fix (guard empty region)
 def fit_and_average(x_data, y_data, start_idx, end_idx):
@@
     x_region = x_data[start_idx:end_idx]
     y_region = y_data[start_idx:end_idx]
 
-    if len(x_region) < 2:
-        avg = float(np.mean(y_region)) if len(y_region) > 0 else 0.0
-        return avg, 0.0, avg
+    if len(x_region) == 0:
+        raise ValueError(f"empty fit region for indices [{start_idx}, {end_idx})")
+    if len(x_region) == 1:
+        avg = float(y_region[0])
+        return avg, 0.0, avg
🤖 Prompt for AI Agents
In `@other/materials_designer/workflows/vbo_polar.pyi` around lines 95 - 107, In
fit_and_average, do not silently return 0.0 for an empty region; instead detect
when x_region and y_region are empty and raise a ValueError describing the empty
fit region, and only use the "avg with zero slope" fallback when the region
contains exactly one point (compute avg = float(np.mean(y_region)) and return
avg, 0.0, avg); keep the regular linear fit logic for len(x_region) >= 2. Ensure
the error message mentions fit_and_average and the start/end indices for
debugging.


slope, intercept, r_value, _, _ = linregress(x_region, y_region)

# Average value of linear fit over the region
# V_avg = (1/L) * integral(slope*x + intercept) = slope*x_mid + intercept
x_mid = (x_region[0] + x_region[-1]) / 2.0
avg_value = slope * x_mid + intercept

return float(avg_value), float(slope), float(intercept)

# Get indices for each slab region in interface profile
slab1_start, slab1_end = get_region_indices(X, z_min_1, z_max_1)
slab2_start, slab2_end = get_region_indices(X, z_min_2, z_max_2)

# Fit interface regions to get average ESP at interface
Va_interface, slope_a, intercept_a = fit_and_average(X, Y, slab1_start, slab1_end)
Vb_interface, slope_b, intercept_b = fit_and_average(X, Y, slab2_start, slab2_end)

# Get indices for slab regions in bulk profiles
slab1_start_left, slab1_end_left = get_region_indices(X_left, z_min_1, z_max_1)
slab2_start_right, slab2_end_right = get_region_indices(X_right, z_min_2, z_max_2)

# Fit bulk material profiles over the same z-ranges as their slabs
Va_bulk_left, _, _ = fit_and_average(X_left, Y_left, slab1_start_left, slab1_end_left)
Vb_bulk_right, _, _ = fit_and_average(X_right, Y_right, slab2_start_right, slab2_end_right)

# Calculate VBO using interface and bulk ESP values
# VBO = (interface difference) - (bulk difference)
VBO = (Vb_interface - Va_interface) - (Vb_bulk_right - Va_bulk_left)

print(f"Interface ESP Slab 1 (Va_interface): {Va_interface:.3f} eV")
print(f"Interface ESP Slab 2 (Vb_interface): {Vb_interface:.3f} eV")
print(f"Bulk ESP Left (Va_bulk): {Va_bulk_left:.3f} eV")
print(f"Bulk ESP Right (Vb_bulk): {Vb_bulk_right:.3f} eV")
print(f"Interface ∆V: {Vb_interface - Va_interface:.3f} eV")
print(f"Bulk ∆V: {Vb_bulk_right - Va_bulk_left:.3f} eV")
print(f"Valence Band Offset (VBO): {VBO:.3f} eV")

# Generate visualization plot
plt.figure(figsize=(10, 6))
plt.plot(X, Y, label="Macroscopic Average Potential", linewidth=2)

# Highlight fitting regions
plt.axvspan(z_min_1, z_max_1, color="red", alpha=0.2, label="Slab 1 Region")
plt.axvspan(z_min_2, z_max_2, color="blue", alpha=0.2, label="Slab 2 Region")

# Plot fitted lines
if slab1_end > slab1_start:
x_fit1 = X[slab1_start:slab1_end]
y_fit1 = slope_a * x_fit1 + intercept_a
plt.plot(x_fit1, y_fit1, color="darkred", linestyle="--", linewidth=2, label="Fit Slab 1")

if slab2_end > slab2_start:
x_fit2 = X[slab2_start:slab2_end]
y_fit2 = slope_b * x_fit2 + intercept_b
plt.plot(x_fit2, y_fit2, color="darkblue", linestyle="--", linewidth=2, label="Fit Slab 2")

# Plot average ESP values
plt.axhline(Va_interface, color="red", linestyle=":", linewidth=2, label=f"Avg ESP Slab 1 = {Va_interface:.3f} eV")
plt.axhline(Vb_interface, color="blue", linestyle=":", linewidth=2, label=f"Avg ESP Slab 2 = {Vb_interface:.3f} eV")

plt.xlabel("z-coordinate (Å)", fontsize=12)
plt.ylabel("Macroscopic Average Potential (eV)", fontsize=12)
plt.title(f"Polar Interface VBO = {VBO:.3f} eV", fontsize=14, fontweight="bold")
plt.legend(loc="best", fontsize=10)
plt.grid(True, alpha=0.3)
plt.tight_layout()
# plt.show()

filename = f"polar_vbo_fit_interface.png"
plt.savefig(filename, dpi=150, bbox_inches="tight")
plt.close()