File size: 2,141 Bytes
4e4ff14 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | import numpy as np
def read_xyz(filename):
coords = []
atoms = []
with open(filename, 'r') as f:
lines = f.readlines()
for line in lines[2:]:
parts = line.split()
atoms.append(parts[0])
coords.append(np.array([float(parts[1]), float(parts[2]), float(parts[3])]))
return atoms, coords
def check_physics():
print("--- 🧪 Scientific Validation ---")
# 1. Load the Generated Crystal
atoms, coords = read_xyz("gen_final.xyz")
# 2. Find the Titanium (Ti) and Oxygens (O)
# Note: In our code, we mapped:
# 22 -> Ti (Titanium)
# 8 -> O (Oxygen)
# 20 -> Ca (Calcium)
ti_indices = [i for i, atom in enumerate(atoms) if atom == "Ti"]
o_indices = [i for i, atom in enumerate(atoms) if atom == "O"]
if not ti_indices or not o_indices:
print("❌ Could not find Ti or O atoms to measure bonds.")
return
print(f"Found {len(ti_indices)} Titanium and {len(o_indices)} Oxygen atoms.")
# 3. Measure Distances
bond_lengths = []
for ti_idx in ti_indices:
ti_pos = coords[ti_idx]
for o_idx in o_indices:
o_pos = coords[o_idx]
# Calculate Euclidean Distance
dist = np.linalg.norm(ti_pos - o_pos)
bond_lengths.append(dist)
# 4. Analyze Results
min_bond = min(bond_lengths)
avg_bond = sum(bond_lengths) / len(bond_lengths)
print(f"\nMeasured Bond Lengths (Ti - O):")
print(f" Minimum: {min_bond:.4f} Å")
print(f" Average: {avg_bond:.4f} Å")
# 5. The "DeepMind" Pass/Fail
# Real Physics: Ti-O bond is typically 1.90 - 2.05 Å
# We allow some error since this is a tiny model trained for 5 minutes
if 1.5 < min_bond < 2.5:
print("\n✅ SUCCESS: The model learned valid chemical bonds!")
print(" (Target range: ~1.9 Å)")
else:
print("\n⚠️ WARNING: Bonds are physically unrealistic.")
print(" (Try training for more epochs or checking the dataset)")
if __name__ == "__main__":
check_physics() |