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from src.api import MaterialsProjectAdapter
from mendeleev import element
class UniversalMaterialsOptimizer:
def __init__(self, host_formula: str, host_element_symbol: str, api_key: str = None):
self.adapter = MaterialsProjectAdapter(api_key)
self.props = self.adapter.get_material_properties(host_formula)
self.base_voltage = self.props['band_gap']
host = element(host_element_symbol)
self.host_radius = host.ionic_radii[0].ionic_radius
self.host_en = host.en_pauling
self.host_symbol = host_element_symbol
def analyze_dopant(self, dopant_symbol: str, concentration: float):
dopant = element(dopant_symbol)
# 1. Physics Calculations
delta_en = dopant.en_pauling - self.host_en
# Simple ionic radius lookup (using first available radius for simplicity)
dop_rad = dopant.ionic_radii[0].ionic_radius if dopant.ionic_radii else self.host_radius
radius_diff = dop_rad - self.host_radius
voltage_gain = 1.5 * concentration * delta_en
predicted_voltage = self.base_voltage + voltage_gain
strain_energy = (radius_diff ** 2) * concentration * 100
# 2. Verdict Logic
status = "Stable"
if strain_energy > 500: status = "Critical Strain - Phase Separation"
elif predicted_voltage < 1.0: status = "Voltage Collapse"
return {
"dopant": dopant_symbol,
"concentration": concentration,
"predicted_voltage": round(predicted_voltage, 3),
"lattice_strain": round(strain_energy, 2),
"stability_status": status,
"data_confidence": "Low (Estimated)" if self.props['is_estimated'] else "High (Real Data)"
}
def analyze_mixture(self, dopant_recipe: dict):
"""
Analyzes a mixture of dopants (Co-doping).
Input: {"Cl": 0.1, "Br": 0.1} -> implies 0.2 total doping
"""
total_conc = sum(dopant_recipe.values())
# 1. Edge Case: Empty Recipe
if total_conc == 0:
return {"error": "Recipe is empty. Please add at least one dopant."}
# 2. Calculate Weighted Properties (Vegard's Law)
weighted_radius = 0.0
weighted_en = 0.0
detailed_breakdown = []
for symbol, amount in dopant_recipe.items():
atom = element(symbol)
# Get radius (default to host radius if data missing to prevent crash)
r = atom.ionic_radii[0].ionic_radius if atom.ionic_radii else self.host_radius
en = atom.en_pauling if atom.en_pauling else self.host_en
# Contribution to the average
fraction = amount / total_conc
weighted_radius += r * fraction
weighted_en += en * fraction
detailed_breakdown.append(f"{symbol} ({amount*100:.1f}%)")
# 3. Compare 'Virtual Dopant' vs Host
radius_diff = weighted_radius - self.host_radius
delta_en = weighted_en - self.host_en
# 4. Physics Engine
# Voltage is boosted by the total amount of dopant * average electronegativity gain
voltage_gain = 1.5 * total_conc * delta_en
predicted_voltage = self.base_voltage + voltage_gain
# Strain is proportional to the mismatch squared * total concentration
strain_energy = (radius_diff ** 2) * total_conc * 100
# 5. Verdict
status = "Stable"
if strain_energy > 500: status = "Critical Strain - Phase Separation"
elif predicted_voltage < 1.0: status = "Voltage Collapse"
return {
"recipe_description": ", ".join(detailed_breakdown),
"total_doping_level": round(total_conc, 3),
"predicted_voltage": round(predicted_voltage, 3),
"lattice_strain": round(strain_energy, 2),
"stability_status": status,
"data_confidence": "Low (Estimated)" if self.props['is_estimated'] else "High (Real Data)"
} |